CN113971605A - Loan risk reduction method and device - Google Patents
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Abstract
The invention discloses a loan risk reduction method and a loan risk reduction device, which relate to the field of artificial intelligence, and the method comprises the following steps: collecting behavior data of loan clients and storing the behavior data in a database; acquiring behavior data of historical bad-account clients from behavior data of loan clients in a database; determining the weight of each kind of behavior data of the historical bad account clients; training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weight is determined; and acquiring the behavior data of the new client, predicting whether the new client is a bad-account client or not based on the bad-account client prediction model and the behavior data of the new client, if so, determining that the client has loan risk, and informing a bank of the prediction result. The invention can realize the prediction of bad account customers through artificial intelligence, and the bank can take measures in advance, thereby reducing the loan risk of the bank.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a loan risk reduction method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, after a bank issues a loan to a client, the condition that the client does not pay back the loan in time and finally forms a bad account still exists, and the risk of the bank after issuing the loan is very large due to the lack of monitoring of the bad account client.
Disclosure of Invention
The embodiment of the invention provides a loan risk reduction method, which is used for solving the technical problem that the risk of a bank after loan is issued is very high due to the lack of monitoring of bad-account customers, and comprises the following steps:
collecting behavior data of loan clients and storing the behavior data in a database;
acquiring behavior data of historical bad-account clients from behavior data of loan clients in a database;
determining the weight of each kind of behavior data of the historical bad account clients;
training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weight is determined;
and acquiring the behavior data of the new client, predicting whether the new client is a bad-account client or not based on the bad-account client prediction model and the behavior data of the new client, if so, determining that the client has loan risk, and informing a bank of the prediction result.
The embodiment of the invention also provides a loan risk reducing device, which is used for solving the technical problem that the risk of a bank after loan is issued is very high due to the lack of monitoring of bad-account customers, and comprises:
the behavior data acquisition module of the loan client is used for acquiring behavior data of the loan client and storing the behavior data in the database;
the bad account client behavior data collection and processing module is used for acquiring the behavior data of the historical bad account clients from the behavior data of the loan clients in the database and determining the weight of each behavior data of the historical bad account clients;
the training and optimizing model module is used for training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customer after the weight is determined;
and the model prediction module is used for acquiring the behavior data of the new client, predicting whether the new client is a bad-account client or not based on the bad-account client prediction model and the behavior data of the new client, if so, determining that the client has loan risk, and informing a bank of the prediction result.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the loan risk reduction method.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the loan risk reduction method described above.
In the embodiment of the invention, compared with the technical scheme that the risk of a bank after loan is issued is very high due to the lack of monitoring on bad-account clients in the prior art, the behavior data of the loan clients are collected and stored in the database; acquiring behavior data of historical bad-account clients from behavior data of loan clients in a database; determining the weight of each kind of behavior data of the historical bad account clients; training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weight is determined; the method comprises the steps of obtaining behavior data of a new client, predicting whether the new client is a bad-account client or not based on a bad-account client prediction model and the behavior data of the new client, if so, determining that the client has a loan risk, and informing a bank of a prediction result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a loan risk reduction method according to an embodiment of the invention;
FIG. 2 is a flow chart of a loan risk reduction method according to an embodiment of the invention;
FIG. 3 is a block diagram of a loan risk reduction apparatus according to an embodiment of the invention;
FIG. 4 is a block diagram of a loan risk reduction apparatus according to an embodiment of the invention;
fig. 5 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Fig. 1 is a flowchart illustrating a loan risk reduction method according to an embodiment of the invention, as shown in fig. 1, the method includes:
step 101: collecting behavior data of loan clients and storing the behavior data in a database;
step 102: acquiring behavior data of historical bad-account clients from behavior data of loan clients in a database;
step 103: determining the weight of each kind of behavior data of the historical bad account clients;
step 104: training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weight is determined;
step 105: and acquiring the behavior data of the new client, predicting whether the new client is a bad-account client or not based on the bad-account client prediction model and the behavior data of the new client, if so, determining that the client has loan risk, and informing a bank of the prediction result.
Specifically, in step 101, behavior data of all loan clients is collected and stored in a database for subsequent collection and processing. The behavior data is as follows: investing in 20 million of a project, buying 10 million of a stock, transferring out 15 million, overdue 3 outstanding loans, selling 10 million of property, etc.
Specifically, in step 102, behavior data of historical bad-account customers is obtained from a system database, wherein the historical behavior data is collected from one month to half a year and marked as bad-account customers. The pattern is shown in table 1:
table 1 historical bad account customer behavior data
Description of the drawings: where 0 represents that the client did not take place the action.
In step 103, according to each row of behavior data in the table above, calculating the proportional weight of the row of behavior data of each client, and dividing the behavior data value of each client by the sum of the row of behavior data values, if the total row of investment items is 100 ten thousand, the proportional weight of the investment item of client 1 is: 20/100 is 0.2.
The calculated pattern is shown in table 2:
table 2 historical bad account client behavior data after calculating weights
In this embodiment of the present invention, in step 104, based on the behavior data of the historical bad-account customer after determining the weight, training and optimizing a bad-account customer prediction model includes:
dividing the behavior data of the historical bad account clients after the weight is determined into training samples and testing samples;
taking behavior data of the historical bad account clients after the weight is determined as training samples as input, and performing bad account client prediction model training by utilizing an SVM classification algorithm to obtain a trained bad account client prediction model;
and taking the behavior data of the historical bad account customers after the weight is determined as the test sample as input, and optimizing the trained bad account customer prediction model to obtain the optimized bad account customer prediction model.
Specifically, the data is divided into two parts, 80% for training the model and 20% for testing the model
And (3) carrying out model training on the processed data by using an SVM classification algorithm, wherein the probability after model prediction is more than 70%, the client is considered to be a bad account client, and the model directly outputs whether the client is the bad account client or not. And testing the correctness of the model by using 20% of data, and continuously optimizing to finally obtain a model with higher correctness.
Specifically, in step 105, the behavior data of the new client is input into the model, the model predicts whether the client is a bad-account client after one month, and if the client is predicted to be a bad-account client, the bank is notified to automatically take corresponding measures, for example, the loan risk of the bank is reduced by measures such as reducing the client amount or freezing funds.
In the embodiment of the present invention, as shown in fig. 2, the method further includes:
step 201: setting loan risk reduction measures;
step 202: notifying a bank of the loan risk mitigation measures.
In an embodiment of the present invention, a loan risk reduction apparatus is further provided, as described in the following embodiments. Since the principle of the device for solving the problems is similar to the loan risk reduction method, the implementation of the device can be referred to the implementation of the loan risk reduction method, and repeated details are not repeated.
Fig. 3 is a block diagram of a loan risk reduction apparatus according to an embodiment of the invention, as shown in fig. 3, the apparatus includes:
the behavior data collecting module 02 of the loan clients is used for collecting behavior data of the loan clients to be stored in a database;
the bad account client behavior data collecting and processing module 04 is used for acquiring the behavior data of the historical bad account clients from the behavior data of the loan clients in the database and determining the weight of each behavior data of the historical bad account clients;
the training and optimizing model module 06 is used for training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customer after the weight is determined;
and the model prediction module 08 is used for acquiring the behavior data of the new client, predicting whether the new client is a bad-account client or not based on the bad-account client prediction model and the behavior data of the new client, if so, determining that the client has loan risk, and informing a bank of the prediction result.
In an embodiment of the present invention, the training and optimization model is specifically configured to:
and training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weights are determined by utilizing an SVM classification algorithm.
In an embodiment of the present invention, the training and optimization model is specifically configured to:
dividing the behavior data of the historical bad account clients after the weight is determined into training samples and testing samples;
taking behavior data of the historical bad account clients after the weight is determined as training samples as input, and performing bad account client prediction model training by utilizing an SVM classification algorithm to obtain a trained bad account client prediction model;
and taking the behavior data of the historical bad account customers after the weight is determined as the test sample as input, and optimizing the trained bad account customer prediction model to obtain the optimized bad account customer prediction model.
In the embodiment of the present invention, as shown in fig. 4, the method further includes:
and the loan risk reduction module 10 is used for setting loan risk reduction measures and informing the bank of the loan risk reduction measures.
Embodiments of the present invention also provide a computer apparatus 500, as shown in fig. 5, comprising a memory 510, a processor 520, and a computer program 530 stored on the memory and executable on the processor, the processor implementing the loan risk reduction method when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the loan risk reduction method described above.
In the embodiment of the invention, compared with the technical scheme that the risk of a bank after loan is issued is very high due to the lack of monitoring on bad-account clients in the prior art, the behavior data of the loan clients are collected and stored in the database; acquiring behavior data of historical bad-account clients from behavior data of loan clients in a database; determining the weight of each kind of behavior data of the historical bad account clients; training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weight is determined; the method comprises the steps of obtaining behavior data of a new client, predicting whether the new client is a bad-account client or not based on a bad-account client prediction model and the behavior data of the new client, if so, determining that the client has a loan risk, and informing a bank of a prediction result.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A loan risk reduction method, comprising:
collecting behavior data of loan clients and storing the behavior data in a database;
acquiring behavior data of historical bad-account clients from behavior data of loan clients in a database;
determining the weight of each kind of behavior data of the historical bad account clients;
training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weight is determined;
and acquiring the behavior data of the new client, predicting whether the new client is a bad-account client or not based on the bad-account client prediction model and the behavior data of the new client, if so, determining that the client has loan risk, and informing a bank of the prediction result.
2. The loan risk reduction method of claim 1, wherein the bad-account client prediction model training and optimization based on the weighted historical bad-account clients' behavioral data comprises:
and training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weights are determined by utilizing an SVM classification algorithm.
3. The loan risk reduction method of claim 2, wherein the bad-account client prediction model training and optimization based on the weighted historical bad-account clients' behavioral data comprises:
dividing the behavior data of the historical bad account clients after the weight is determined into training samples and testing samples;
taking behavior data of the historical bad account clients after the weight is determined as training samples as input, and performing bad account client prediction model training by utilizing an SVM classification algorithm to obtain a trained bad account client prediction model;
and taking the behavior data of the historical bad account customers after the weight is determined as the test sample as input, and optimizing the trained bad account customer prediction model to obtain the optimized bad account customer prediction model.
4. The loan risk reduction method of claim 1, further comprising:
setting loan risk reduction measures;
notifying a bank of the loan risk mitigation measures.
5. A loan risk reduction apparatus, comprising:
the behavior data acquisition module of the loan client is used for acquiring behavior data of the loan client and storing the behavior data in the database;
the bad account client behavior data collection and processing module is used for acquiring the behavior data of the historical bad account clients from the behavior data of the loan clients in the database and determining the weight of each behavior data of the historical bad account clients;
the training and optimizing model module is used for training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customer after the weight is determined;
and the model prediction module is used for acquiring the behavior data of the new client, predicting whether the new client is a bad-account client or not based on the bad-account client prediction model and the behavior data of the new client, if so, determining that the client has loan risk, and informing a bank of the prediction result.
6. The loan risk reduction apparatus of claim 5, wherein the training and optimization model is specifically configured to:
and training and optimizing a bad account customer prediction model based on the behavior data of the historical bad account customers after the weights are determined by utilizing an SVM classification algorithm.
7. The loan risk reduction apparatus of claim 6, wherein the training and optimization model is specifically configured to:
dividing the behavior data of the historical bad account clients after the weight is determined into training samples and testing samples;
taking behavior data of the historical bad account clients after the weight is determined as training samples as input, and performing bad account client prediction model training by utilizing an SVM classification algorithm to obtain a trained bad account client prediction model;
and taking the behavior data of the historical bad account customers after the weight is determined as the test sample as input, and optimizing the trained bad account customer prediction model to obtain the optimized bad account customer prediction model.
8. The loan risk reduction arrangement according to claim 5, further comprising:
and the loan risk reduction module is used for setting loan risk reduction measures and informing the bank of the loan risk reduction measures.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the loan risk reduction method of any of claims 1 to 4.
10. A computer-readable storage medium having stored thereon a computer program, the program, when executed by a processor, implementing the steps of the loan risk reduction method of any of claims 1 to 4.
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| CN202111263535.4A CN113971605A (en) | 2021-10-28 | 2021-10-28 | Loan risk reduction method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110738564A (en) * | 2019-10-16 | 2020-01-31 | 信雅达系统工程股份有限公司 | Post-loan risk assessment method and device and storage medium |
| CN111192131A (en) * | 2019-12-12 | 2020-05-22 | 上海淇玥信息技术有限公司 | Financial risk prediction method and device and electronic equipment |
| CN111324862A (en) * | 2020-02-10 | 2020-06-23 | 深圳华策辉弘科技有限公司 | Method and system for monitoring behavior in loan |
| CN112232947A (en) * | 2020-10-23 | 2021-01-15 | 中国工商银行股份有限公司 | Loan risk prediction method and device |
| CN113554510A (en) * | 2021-08-05 | 2021-10-26 | 百维金科(上海)信息科技有限公司 | Loan user default real-time monitoring system |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110738564A (en) * | 2019-10-16 | 2020-01-31 | 信雅达系统工程股份有限公司 | Post-loan risk assessment method and device and storage medium |
| CN111192131A (en) * | 2019-12-12 | 2020-05-22 | 上海淇玥信息技术有限公司 | Financial risk prediction method and device and electronic equipment |
| CN111324862A (en) * | 2020-02-10 | 2020-06-23 | 深圳华策辉弘科技有限公司 | Method and system for monitoring behavior in loan |
| CN112232947A (en) * | 2020-10-23 | 2021-01-15 | 中国工商银行股份有限公司 | Loan risk prediction method and device |
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