CN111461865B - Data analysis method and device - Google Patents
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Abstract
The invention discloses a data analysis method and a data analysis device, which comprise the steps of screening a high-risk target object, and acquiring consumption information of the high-risk target object in a first time period and basic information of a client to which the target object belongs; inputting consumption information of a high-risk target object in a first time period and basic information of a client to which the high-risk target object belongs into a preset consumption prediction model to obtain consumption information of the high-risk target object in a second time period; and obtaining a scoring result of the high-risk target object based on real consumption information of the high-risk target object in the second time period, predicted consumption information of the target object in the second time period and a preset scoring model, and determining whether the high-risk target object has abnormal consumption conditions or not based on the relationship between the scoring of the high-risk target object and a preset threshold value. In this way, the borrowed risk possibly occurring in the credit card can be found in time, so that the risk born by the bank is reduced as much as possible.
Description
Technical Field
The present invention relates to the field of finance, and in particular, to a data analysis method and apparatus.
Background
With the improvement of living standard and the transition of consumption concept, credit cards are becoming one way for people to consume. When a bank issues a credit card, the bank sets the credit card amount based on the repayment capability of the customer in order to reduce the risk.
However, in real life, there is often a case where a credit card is borrowed to another person, and this action is likely to increase the repayment pressure of the customer, so that the customer cannot repayment on time, which in any case increases the risk of the bank.
Disclosure of Invention
In view of the above, the embodiment of the invention discloses a data analysis method and a data analysis device, which predict whether the risk of abnormal consumption occurs or not through analysis of credit card consumption conditions.
The embodiment of the invention discloses a data analysis method, which comprises the following steps:
acquiring repayment information of a target object and identity information of a user corresponding to the target object; the target object is a lending product;
screening a high-risk target object based on repayment information of the target object and identity information of a user to which the target object belongs; the high-risk target object indicates that a target object with abnormal consumption possibly exists, and consumption information of the high-risk target object in a first time period and basic information of a client to which the target object belongs are acquired;
inputting the consumption information of the high-risk target object in the first time period and the basic information of the client to which the high-risk target object belongs into a preset consumption prediction model to obtain the consumption information of the high-risk target object in the second time period;
acquiring real consumption information of a high-risk target object in a second time period;
obtaining a scoring result of the high-risk target object based on the real consumption information, the predicted consumption information and the preset scoring model of the high-risk target object in the second time period; the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set;
and determining whether the high-risk target object has abnormal consumption conditions or not based on the relation between the scores of the high-risk target object and a preset threshold value.
Optionally, the screening the high-risk target object based on the repayment information of the target object and the identity information of the user to which the target object belongs includes:
judging whether repayment information of the target object is consistent with identity information of a user to which the target object belongs;
and if the repayment information of the target object is inconsistent with the identity information of the user to which the target object belongs, indicating that the target object is a high-risk target object.
Optionally, the training process of the scoring model includes:
constructing a neural network model;
acquiring a second data set; the second data set comprises real consumption information and predicted consumption information of the high-risk target object in the same time period, and the target object in the second data set is marked with a grading result;
acquiring an initial parameter value of the neural network;
training the neural network model based on the initial parameter values and a second data set of the neural network.
Optionally, the training process of the scoring model includes:
the method comprises the steps of obtaining a calculation method for calculating the matching degree of each consumption object in real consumption information and each consumption object in preset consumption information;
determining a scoring rule of each consumption object in the real consumption information;
determining scoring statistical rules of all consumption objects in the real consumption information;
training a preset expert system based on a calculation method of the matching degree of each consumption object in the real consumption information and each consumption object in the preset consumption information, a scoring rule of the consumption objects in the real consumption information and scoring statistical rules of all the consumption objects in the real consumption information.
Optionally, the determining whether the high-risk client has an abnormal consumption condition based on the relationship between the score of the high-risk target object and a preset threshold value includes:
if the score of the high-risk target object is larger than a preset threshold value, the consumption behavior of the target object is indicated to be abnormal;
and if the score of the high-risk target object is smaller than or equal to a preset threshold value, the consumption behavior of the target object is abnormal.
Optionally, the method further comprises:
determining a total number of consumption objects included in the real consumption information of the second time period;
the threshold is determined based on a total number of consumption objects included in the real consumption information for the second period of time.
Optionally, the method further comprises:
under the condition that the consumption abnormal behavior of the target object is detected, monitoring the use dynamic state of the target object;
when the target object is monitored to be reused, acquiring face information of a consumer using the target object;
judging whether the face information of the consumer is consistent with the face information of the user to which the preset target object belongs;
if the face information of the consumer is inconsistent with the face information of the user belonging to the preset target object, sending a consumption abnormality reminder to the user belonging to the target object.
The embodiment of the invention discloses a data analysis device, which comprises:
the first acquisition unit is used for acquiring repayment information of the target object and identity information of a user corresponding to the target object; the target object is a lending product;
the screening unit is used for screening the high-risk target object based on repayment information of the target object and identity information of a user to which the target object belongs; the high-risk target object indicates that a target object with abnormal consumption possibly exists, and consumption information of the high-risk target object in a first time period and basic information of a client to which the target object belongs are acquired;
the second acquisition unit is used for acquiring consumption information of the high-risk target object in the first time period and basic information of a client to which the target object belongs;
the consumption prediction model is used for inputting the consumption information of the high-risk target object in the first time period and the basic information of the client to which the high-risk target object belongs into a preset consumption prediction model to obtain the consumption information of the high-risk target object in the second time period;
the third acquisition unit is used for acquiring real consumption information of the high-risk target object in the second time period;
the scoring result determining unit is used for obtaining the scoring result of the high-risk target object based on the real consumption information, the predicted consumption information and the preset scoring model of the high-risk target object in the second time period; the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set;
the abnormal consumption identification unit is used for determining whether the high-risk target object has abnormal consumption conditions or not based on the relation between the scores of the high-risk target object and a preset threshold value.
Optionally, the screening unit includes:
the first screening subunit is used for judging whether the repayment information of the target object is consistent with the identity information of the user to which the target object belongs;
and the second screening subunit is used for indicating that the target object is a high-risk target object if the repayment information of the target object is inconsistent with the identity information of the user to which the target object belongs.
Optionally, the method further comprises:
a training unit of the first scoring model, configured to:
constructing a neural network model;
acquiring a second data set; the second data set comprises real consumption information and predicted consumption information of the high-risk target object in the same time period, and the target object in the second data set is marked with a grading result;
acquiring an initial parameter value of the neural network;
training the neural network model based on the initial parameter values and a second data set of the neural network.
The embodiment of the invention discloses a data analysis method, which comprises the steps of screening a high-risk target object based on repayment information of the target object and identity information of a user to which the target object belongs, so that the data size of the target object to be detected is reduced, and the data processing efficiency is improved. Then, obtaining consumption information of a high-risk target object in a first time period and basic information of a client to which the target object belongs; inputting the consumption information of the high-risk target object in the first time period and the basic information of the client to which the high-risk target object belongs into a preset consumption prediction model to obtain the consumption information of the high-risk target object in the second time period; and obtaining a scoring result of the high-risk target object based on the real consumption information of the high-risk target object in the second time period, the predicted consumption information of the target object in the second time period and a preset scoring model, wherein the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set. And determining whether the high-risk target object has abnormal consumption conditions or not based on the relation between the scores of the high-risk target objects and a preset threshold value. In this way, the borrowed risk possibly occurring in the credit card can be found in time, so that the risk born by the bank is reduced as much as possible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a flow chart of a data analysis method according to an embodiment of the present invention;
FIG. 2 shows a flowchart of a score model training method according to an embodiment of the present invention;
FIG. 3 is a flowchart of another training method of a scoring model according to an embodiment of the present invention;
fig. 4 shows a schematic structural diagram of a data analysis device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flow chart of a data analysis method provided by an embodiment of the present invention is shown, and in this embodiment, the method includes:
s101: acquiring repayment information of the target object and information of a client identity of the target object; the target object is a lending product;
in this embodiment, the debit product includes a credit card, or other credit product that may enable installment.
In this embodiment, the repayment information of the target object includes information of a repayment party that repayment the credit product, specifically, identity information of the repayment party. The identity information of the user to which the target object belongs refers to the identity information of the user registered when the credit type product is applied. The identity information obtaining manner of the repayment party may include various types, which are not limited in this embodiment, and may include, for example, the following two manners:
the method comprises the steps of receiving identity information of a repayment party uploaded by a user;
obtaining a repayment mode of the target object;
and calling the identity information of the repayment party of the target object based on the repayment mode of the target object.
Illustrating: the repayment method may include: and if the repayment mode is a bank card repayment mode, the identity information of the customer to which the repayment bank card account belongs can be called. If the repayment mode is a WeChat repayment mode or a payment device repayment mode, the client information of repayment WeChat or the client information of repayment payment device can be called.
S102: screening a high-risk target object based on repayment information of the target object and identity information of a user to which the target object belongs; the high risk target object represents a target object that may have abnormal consumption;
in this embodiment, in order to reduce the risk due to the borrowing of the credit card, the bank backend system needs to monitor the usage of all the credit cards, but the number of customers who own the credit cards is very large, in which case the processing efficiency of the data is easily reduced.
And the applicant has also found that in the repayment of credit cards, the customer repayment by means of the asset under his own name, and also by means of other assets under his name, for example: the customers can pay back the credit card through the bank card under the customer name, and can pay back the bank card through the bank card under the other name, and the bank is not limited on how to pay back. However, the applicant can know through big data analysis that when a customer pays through property under other names, the customer has a high possibility of abnormal consumption, namely, the customer is likely to borrow the credit card to other people.
Therefore, in order to improve the efficiency of data processing, the client may be screened to screen out the target object with higher risk, which specifically includes:
whether repayment information of the target object is consistent with identity information of a user to which the target object belongs;
and if the repayment information of the target object is inconsistent with the identity information of the user to which the target object belongs, indicating that the target object is a high-risk target object.
In this embodiment, the high-risk target object is a target object with a relatively high possibility of having an abnormal consumption, and in order to further confirm whether the high-risk target object has an abnormal consumption condition, the following operations are performed for each high-risk target object:
s103: acquiring consumption information of a high-risk target object in a first time period and basic information of a client to which the target object belongs;
in this embodiment, the consumption information in the first period is consumption information of the target object (e.g., credit card) in a certain period in the history time.
Wherein the consumption information includes commodity information purchased by the user through consumption, and taking credit card consumption as an example, the historical consumption information can be expressed as commodity information purchased by the user through credit card consumption.
The client basic information to which the target object belongs may include, for example: user age, family condition, academic, income level, hobbies, etc.
Illustrating: if the target object is a credit card, the information of the client corresponding to the target object indicates the basic information of the client registered when the credit card is applied, for example, may include: user age, family condition, academic, income level, hobbies, etc.
S104: inputting the consumption information of the high-risk target object in the first time period and the basic information of the client to which the high-risk target object belongs into a preset consumption prediction model to obtain the consumption information of the high-risk target object in the second time period;
the consumption prediction model is obtained after training based on a first data set, wherein the first data set comprises consumption information of a high-risk target object in a third time period and basic information of a client to which the high-risk target object belongs;
in this embodiment, the consumption information in the third period represents the consumption information of the target object in a certain period of time in the history time.
Wherein the first time period is expressed as a time period to be predicted, and the third time period is earlier than the first time period.
Illustrating: if the consumption information of 9 months in the credit card 2020 is to be predicted, the second time period may be understood as 9 months in the year 2020, the first time period and the third time period are each a history time period earlier than 9 months, and the third time period is earlier than the first time period, for example, the first time period may be a time period from 1 month to 7 months in the year 2020, and the second time period may be 8 months in the year 2020.
In this embodiment, the consumption prediction model is obtained by training a preset machine learning model through consumption information of the target object and basic information of a client to which the target object belongs in the third time period. The preset machine learning model can be any model capable of predicting the consumption situation through training. In this embodiment, the machine learning model is not limited, and may be a convolutional neural network model, an SVM model, or a random forest model.
S105: acquiring real consumption information of a high-risk target object in a second time period;
in this embodiment, the actual consumption information in the second period of time is expressed as the actual consumption condition of the target object in the second period of time.
Illustrating: if the target object is a credit card, the consumption information can be obtained through a consumption record of the credit card, and then the real consumption information in the second time period can be obtained through a consumption record of the credit card in the second time period.
S106: obtaining a scoring result of the high-risk target object based on the real consumption information, the predicted consumption information and the preset scoring model of the high-risk target object in the second time period; the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set;
in this embodiment, the scoring model may be obtained through multiple training modes, which is not limited in this embodiment, and preferably, two training modes are provided in this embodiment as follows:
a first method,
S201: constructing a neural network model;
in this embodiment, the neural network model may include a plurality of types, and is not limited in this embodiment.
S202: acquiring a second data set; the second data set comprises real consumption information and predicted consumption information of the high-risk target object in the same time period, and the target object in the second data set is marked with a grading result;
in this embodiment, the target object may be marked by a preset rule, where the rule is based on a correlation principle between real consumption information and predicted consumption information, and may be specifically understood as:
calculating the matching degree of each consumption object in the real consumption information and the consumption object in the predicted consumption information;
scoring each consumption object in the real consumption information based on the matching degree of each consumption object in the real consumption information and the consumption object in the predicted consumption information;
and calculating the score of the target object based on the score of each consumption object in the real consumption information.
For each consumer object in the real consumer information, the higher the matching degree of the consumer object and the consumer object in the predicted consumer information is, the higher the score is, the lower the matching degree is, the lower the score is, and if the consumer object is not matched completely, the score can be zero score or a negative value.
In addition, each consumption object in the real consumption information can be scored through a preset scoring model, wherein the preset scoring model can be obtained through training of a third data set, the third data set comprises real consumption information and predicted consumption information of the target object in the same time period, and each consumption object in the real consumption information is scored and marked.
Or, the matching degree of the real consumption information and the predicted consumption information can be calculated by a method of evaluating the function.
S203: acquiring an initial parameter value of the neural network;
in this embodiment, the initial parameter values of the neural network may include: initial weights and thresholds, etc.
S204: training the neural network model based on the initial parameter values and a second data set of the neural network.
A second method,
S301: the method comprises the steps of obtaining a calculation method for calculating the matching degree of each consumption object in the real consumption information and each consumption object in the predicted consumption information;
in this embodiment, a plurality of methods may be set, and the matching degree between each consumption object in the real consumption information and each consumption object in the predicted consumption information is calculated, which is not limited in this embodiment. Preferably, the following method may be employed:
method one, for each consumption object in the real consumption information:
acquiring attribute information of a consumption object in the real consumption information;
acquiring attribute information of all consumption objects in the predicted consumption information;
and calculating the matching degree of the attribute information of the consumption object in the real consumption information and the attribute information of the consumption object contained in the preset consumption information.
Calculating the matching degree of each consumption object in the real consumption information and each consumption object in the predicted consumption information through the trained classification model;
the training method of the classification model comprises the following steps:
acquiring a third data set; the third dataset comprises: two body data sets; each ontology dataset contains different consumption objects;
generating a matching relationship between each consumption object in the two ontology data sets;
calculating the matching degree between all the consumption objects based on the attribute information of different dimensions, so as to obtain a third data set;
training the classification model based on the third data set, so as to obtain a method for calculating the matching degree of the consumption objects in different consumption information.
S302: determining a scoring rule of each consumption object in the real consumption information; wherein the scoring rule is determined based on a degree of matching of each consumer object in the real consumer information with each consumer object in the predicted consumer information. Illustrating: for consumer objects with high matching degree, a higher result may be set, for consumer objects with lower matching degree, a lower score may be set, and for consumer objects that cannot match, a 0 score or negative score may be set.
S303: determining a statistical rule of scores of all consumption objects in the real consumption information;
the statistical rule is expressed as how scores of all the consumption objects in the real consumption information are counted.
For example: the scores of all the consumption objects in the real consumption information may be added together or all the consumption objects in the real consumption information may be weighted together.
S304: training a preset expert system based on a calculation method of the matching degree of each consumption object in the real consumption information and each consumption object in the predicted consumption information, a scoring rule of each consumption object in the real consumption information and a statistics rule of scores of all consumption objects in the real consumption information.
The expert system obtained by the method can calculate the final score of the target object after inputting the real consumption information and the predicted consumption information of the target object.
S107: and determining whether the high-risk client has abnormal consumption conditions or not based on the relation between the scores of the high-risk target objects and a preset threshold value.
In this embodiment, since the total score of the high-risk target object is related to the consumption object in the real consumption situation, the consumption object in the real consumption situation is constantly changed in different scenes or different periods of time, and the change includes a change in the number of consumption objects. Therefore, in the case that the number of consumption objects included in the actual consumption situation is changed, if the preset threshold value is not changed, the accuracy of the calculated similarity result will be affected, and in order to improve the accuracy of the final determination result, the preset threshold value may be changed based on the change of the number of consumption objects included in the actual consumption situation, based on this, the embodiment further includes:
determining a total number of consumption objects included in the real consumption information of the second time period;
the threshold is determined based on a total number of consumption objects included in the real consumption information for the second period of time.
In this embodiment, based on the relationship between the score of the high-risk target object and the preset threshold, the determining whether the consumption behavior of the user is abnormal includes two cases as follows:
if the score of the high-risk target object is larger than a preset threshold value, the consumption behavior of the target object is indicated to be abnormal;
and if the score of the high-risk target object is smaller than or equal to a preset threshold value, the consumption behavior of the target object is abnormal.
For the credit card, if the consumption behavior of the user is abnormal, the current credit card can be considered to have the risk of being used by others.
In this embodiment, when detecting that the target object has abnormal consumption behavior, face information of a consumer is obtained when detecting that the target object is used again; identifying face information of a consumer, and judging whether the face information of the consumer is consistent with face information of a user to which a target object belongs; if the consumer information is inconsistent with the user information of the target object, verifying whether the consumer accords with a preset identity.
The preset identity may include: the user to whom the target object belongs sets a person capable of consuming the target object or a person belonging to third generation relatives with the user to whom the target object belongs.
If the consumer is detected not to be the user of the target object and the consumer does not accord with the preset identity, sending a consumption abnormality reminder to the user of the target object.
In this embodiment, when it is detected that the credit card is consumed again, face information of the consumer may be obtained by retrieving a camera of the shop.
In this embodiment, first, the high-risk target object is screened based on the repayment information of the target object and the identity information of the user to which the target object belongs, so that the data size of the target object to be detected is reduced, and the data processing efficiency is improved. Then, obtaining consumption information of a high-risk target object in a first time period and basic information of a client to which the target object belongs; inputting the consumption information of the high-risk target object in the first time period and the basic information of the client to which the high-risk target object belongs into a preset consumption prediction model to obtain the consumption information of the high-risk target object in the second time period; and obtaining a scoring result of the high-risk target object based on the real consumption information of the high-risk target object in the second time period, the predicted consumption information of the target object in the second time period and a preset scoring model, wherein the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set. And determining whether the high-risk target object has abnormal consumption conditions or not based on the relation between the scores of the high-risk target objects and a preset threshold value. In this way, the borrowed risk possibly occurring in the credit card can be found in time, so that the risk born by the bank is reduced as much as possible.
Referring to fig. 4, a schematic structural diagram of a data analysis device according to an embodiment of the present invention is shown, where in this embodiment, the device includes:
a first obtaining unit 401, configured to obtain payment information of a target object and identity information of a user corresponding to the target object; the target object is a lending product;
a screening unit 402, configured to screen a high-risk target object based on payment information of the target object and identity information of a user to which the target object belongs; the high-risk target object indicates that a target object with abnormal consumption possibly exists, and consumption information of the high-risk target object in a first time period and basic information of a client to which the target object belongs are acquired;
a second obtaining unit 403, configured to obtain consumption information of a high-risk target object in a first period of time and basic information of a client to which the target object belongs;
the consumption prediction model 404 is configured to input consumption information of the high-risk target object in a first period and basic information of a client to which the high-risk target object belongs into a preset consumption prediction model, so as to obtain consumption information of the high-risk target object in a second period;
a third obtaining unit 405, configured to obtain real consumption information of the high risk target object in the second period;
a scoring result determining unit 406, configured to obtain a scoring result of the high-risk target object based on the real consumption information, the predicted consumption information and the preset scoring model of the high-risk target object in the second time period; the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set;
the abnormal consumption identifying unit 407 is configured to determine whether an abnormal consumption situation exists in the high-risk target object based on a relationship between a score of the high-risk target object and a preset threshold.
Optionally, the screening unit includes:
the first screening subunit is used for judging whether the repayment information of the target object is consistent with the identity information of the user to which the target object belongs;
and the second screening subunit is used for indicating that the target object is a high-risk target object if the repayment information of the target object is inconsistent with the identity information of the user to which the target object belongs.
Optionally, the method further comprises:
a training unit of the first scoring model, configured to:
constructing a neural network model;
acquiring a second data set; the second data set comprises real consumption information and predicted consumption information of the high-risk target object in the same time period, and the target object in the second data set is marked with a grading result;
acquiring an initial parameter value of the neural network;
training the neural network model based on the initial parameter values and a second data set of the neural network.
Optionally, the training unit of the second scoring model is configured to:
the method comprises the steps of obtaining a calculation method for calculating the matching degree of each consumption object in real consumption information and each consumption object in preset consumption information;
determining a scoring rule of each consumption object in the real consumption information;
determining scoring statistical rules of all consumption objects in the real consumption information;
training a preset expert system based on a calculation method of the matching degree of each consumption object in the real consumption information and each consumption object in the preset consumption information, a scoring rule of the consumption objects in the real consumption information and scoring statistical rules of all the consumption objects in the real consumption information.
Optionally, the abnormal consumption identifying unit is specifically configured to:
if the score of the high-risk target object is larger than a preset threshold value, the consumption behavior of the target object is indicated to be abnormal;
and if the score of the high-risk target object is smaller than or equal to a preset threshold value, the consumption behavior of the target object is abnormal.
Optionally, the method further comprises:
determining a total number of consumption objects included in the real consumption information of the second time period;
the threshold is determined based on a total number of consumption objects included in the real consumption information for the second period of time.
Optionally, the method further comprises:
an abnormality alert detection unit configured to:
under the condition that the consumption abnormal behavior of the target object is detected, monitoring the use dynamic state of the target object;
when the target object is monitored to be reused, acquiring face information of a consumer using the target object;
judging whether the face information of the consumer is consistent with the face information of the user to which the preset target object belongs;
if the face information of the consumer is inconsistent with the face information of the user belonging to the preset target object, sending a consumption abnormality reminder to the user belonging to the target object.
According to the device, the high-risk target object is screened based on the repayment information of the target object and the identity information of the user to which the target object belongs, so that the data size of the target object to be detected is reduced, and the data processing efficiency is improved. Then, obtaining consumption information of a high-risk target object in a first time period and basic information of a client to which the target object belongs; inputting the consumption information of the high-risk target object in the first time period and the basic information of the client to which the high-risk target object belongs into a preset consumption prediction model to obtain the consumption information of the high-risk target object in the second time period; and obtaining a scoring result of the high-risk target object based on the real consumption information of the high-risk target object in the second time period, the predicted consumption information of the target object in the second time period and a preset scoring model, wherein the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set. And determining whether the high-risk target object has abnormal consumption conditions or not based on the relation between the scores of the high-risk target objects and a preset threshold value. In this way, the borrowed risk possibly occurring in the credit card can be found in time, so that the risk born by the bank is reduced as much as possible.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method of data analysis, comprising:
acquiring repayment information of the target object and identity information of a user to which the target object belongs; the target object is a lending product;
screening a high-risk target object based on repayment information of the target object and identity information of a user to which the target object belongs; the high-risk target object represents a target object which possibly has abnormal consumption, and consumption information of the high-risk target object in a first time period and basic information of a user to which the target object belongs are acquired;
inputting the consumption information of the high-risk target object in the first time period and the basic information of the user to which the high-risk target object belongs into a preset consumption prediction model to obtain the consumption information of the high-risk target object in the second time period;
acquiring real consumption information of a high-risk target object in a second time period;
obtaining a scoring result of the high-risk target object based on the real consumption information, the predicted consumption information and the preset scoring model of the high-risk target object in the second time period; the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set;
determining whether an abnormal consumption condition exists in the high-risk target object or not based on the relation between the score of the high-risk target object and a preset threshold value;
the screening the high-risk target object based on the repayment information of the target object and the identity information of the user to which the target object belongs includes:
judging whether repayment information of the target object is consistent with identity information of a user to which the target object belongs;
and if the repayment information of the target object is inconsistent with the identity information of the user to which the target object belongs, indicating that the target object is a high-risk target object.
2. The method of claim 1, wherein the training process of the scoring model comprises:
constructing a neural network model;
acquiring a second data set; the second data set comprises real consumption information and predicted consumption information of the high-risk target object in the same time period, and the target object in the second data set is marked with a grading result;
acquiring an initial parameter value of the neural network;
training the neural network model based on the initial parameter values and a second data set of the neural network.
3. The method of claim 1, wherein the training process of the scoring model comprises:
the method comprises the steps of obtaining a calculation method for calculating the matching degree of each consumption object in real consumption information and each consumption object in preset consumption information;
determining a scoring rule of each consumption object in the real consumption information;
determining scoring statistical rules of all consumption objects in the real consumption information;
training a preset expert system based on a calculation method of the matching degree of each consumption object in the real consumption information and each consumption object in the preset consumption information, a scoring rule of each consumption object in the real consumption information and a scoring statistical rule of all consumption objects in the real consumption information.
4. The method of claim 1, wherein determining whether an abnormal consumption condition exists for the high risk target object based on a relationship between a score of the high risk target object and a preset threshold value, comprises:
if the score of the high-risk target object is larger than a preset threshold value, the consumption behavior of the target object is indicated to be abnormal;
and if the score of the high-risk target object is smaller than or equal to a preset threshold value, the consumption behavior of the target object is abnormal.
5. The method as recited in claim 1, further comprising:
determining a total number of consumption objects included in the real consumption information of the second time period;
the threshold is determined based on a total number of consumption objects included in the real consumption information for the second period of time.
6. The method as recited in claim 1, further comprising:
under the condition that the consumption abnormal behavior of the target object is detected, monitoring the use dynamic state of the target object;
when the target object is monitored to be reused, acquiring face information of a consumer using the target object;
judging whether the face information of the consumer is consistent with the face information of the user to which the preset target object belongs;
and if the face information of the consumer is inconsistent with the face information of the user belonging to the preset target object, sending a consumption abnormality reminder to the user belonging to the target object.
7. A data analysis device, comprising:
the first acquisition unit is used for acquiring repayment information of the target object and identity information of a user to which the target object belongs; the target object is a lending product;
the screening unit is used for screening the high-risk target object based on repayment information of the target object and identity information of a user to which the target object belongs; the high risk target object represents a target object that may have abnormal consumption;
the second acquisition unit is used for acquiring consumption information of the high-risk target object in the first time period and basic information of a user to which the target object belongs;
the consumption prediction model is used for inputting the consumption information of the high-risk target object in the first time period and the basic information of the user to which the high-risk target object belongs to obtain the consumption information of the high-risk target object in the second time period;
the third acquisition unit is used for acquiring real consumption information of the high-risk target object in the second time period;
the scoring result determining unit is used for obtaining the scoring result of the high-risk target object based on the real consumption information, the predicted consumption information and the preset scoring model of the high-risk target object in the second time period; the scoring result of the target object is determined based on the correlation between the real consumption information and the predicted consumption information, and the scoring model is obtained after training through a preset second data set;
the abnormal consumption identification unit is used for determining whether the high-risk target object has abnormal consumption conditions or not based on the relation between the scores of the high-risk target object and a preset threshold value;
wherein, the screening unit includes:
the first screening subunit is used for judging whether the repayment information of the target object is consistent with the identity information of the user to which the target object belongs;
and the second screening subunit is used for indicating that the target object is a high-risk target object if the repayment information of the target object is inconsistent with the identity information of the user to which the target object belongs.
8. The apparatus as recited in claim 7, further comprising:
a training unit of the first scoring model, configured to:
constructing a neural network model;
acquiring a second data set; the second data set comprises real consumption information and predicted consumption information of the high-risk target object in the same time period, and the target object in the second data set is marked with a grading result;
acquiring an initial parameter value of the neural network;
training the neural network model based on the initial parameter values and a second data set of the neural network.
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| US11238518B2 (en) * | 2017-12-14 | 2022-02-01 | Wells Fargo Bank, N.A. | Customized predictive financial advisory for a customer |
| CN109785162A (en) * | 2018-12-13 | 2019-05-21 | 平安科技（深圳）有限公司 | Medical insurance method for detecting abnormality, device, equipment and computer storage medium |
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| CN107679982A (en) * | 2017-09-29 | 2018-02-09 | 电子科技大学 | A kind of credit card risk checking method based on point process |
| CN110895758A (en) * | 2019-12-02 | 2020-03-20 | 中国银行股份有限公司 | Screening method, device and system for credit card account with cheating transaction |
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