CN118154303A - Differential adjustment method and system based on data analysis - Google Patents
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Abstract
The invention provides a differential adjusting method and a differential adjusting system based on data analysis, which belong to the technical field of data processing and specifically comprise the following steps: the method comprises the steps of dividing users into a first class of target users and a second class of target users based on user population of the users, determining credit support times of the users on a specific credit platform through credit support data of the users on the specific credit platform when the users are the second class of target users, determining the credit support frequency of the users by combining the support limits of the users on different credit support times, and determining the adjustment strategy of loan interest rate of the users through the credit support frequency of the users and the credit risk of the users when the users are adjustable users based on the credit support frequency of the users, so that effective adjustment of different types of loan utilization is realized.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a differential adjusting method and system based on data analysis.
Background
Loan interest rate is an effective means of asset structure tuning and promoting balance growth in credit scenarios. Different from credit elements such as the line, improper use may cause cost consumption and marketing resource waste, for example, a natural conversion user does not need price reduction and activation, price reduction is performed on a user with higher line use rate, new borrowing and old borrowing are also caused, and larger profit loss is caused.
In the patent CN202311694009.2, on the basis of analysis and processing based on the electric power data, the prediction and analysis of the interest rate are performed, but the difference of the characteristics of the user is not considered, so that the interest rate cannot be accurately and pertinently adjusted.
Aiming at the technical problems, the invention provides a differential adjusting method and a differential adjusting system based on data analysis, and the method can effectively distinguish net benefits brought by a interest rate adjusting means based on user characteristics, and can screen optimal guest groups to realize cost reduction and synergy.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
According to one aspect of the present invention, a differential adjustment method based on data analysis is provided.
The differential adjusting method based on data analysis is characterized by comprising the following steps of:
Determining credit branch data of a user on different credit platforms according to user characteristics of the user, determining credit risk of the user according to the credit branch data, determining a user population of the user based on the user characteristics of the user when the credit risk of the user meets requirements, and dividing the user into a class-one target user and a class-two target user based on the user population of the user;
When the user is a target user, acquiring browsing data of a credit account of the user on a specific credit platform, and determining an adjustment strategy of the loan interest rate of the user by combining the credit risk of the user;
When the user is a class-II target user, the credit count number of the user on a specific credit platform is determined through the credit count data of the user on the specific credit platform, the credit limit of the user is determined by combining the credit limit of the user on different credit count numbers, and when the user is determined to be an adjustable user based on the credit limit of the user, the adjustment strategy of the loan interest rate of the user is determined through the credit limit of the user and the credit risk of the user.
The invention has the beneficial effects that:
1. The method has the advantages that the users are divided into one type of target users and two types of target users based on the user population of the users, the division of the user population is realized from the perspective of user characteristics, the difference of the demand for interest rate adjustment caused by the fact that whether the users have credit account use difference is fully considered, and the influence of interest rate adjustment on the benefits of the credit platform is reduced on the basis of guaranteeing the use effectiveness of the allowance funds of the credit platform.
2. The credit risk of the user is singly considered by utilizing the browsing data of the credit account and the adjustment strategy of the loan interest rate of the credit risk determination user, and the screening of the user with higher demand for the use of the credit account is realized by further combining the browsing data of the credit account of the user, so that a foundation is laid for the targeted generation of the differentiated adjustment strategy of the loan interest rate.
3. The adjustment strategy of the loan interest rate of the user is determined through the credit limit utilization frequency of the user and the credit risk, so that the classified adjustment of the user from the angles of the credit risk and the credit limit utilization frequency is realized, the reliable screening of the user with less credit limit utilization or less credit limit utilization times is ensured, the interest rate adjustment is carried out on the user in a targeted manner, and the fund benefit of a credit platform is improved.
A further technical solution is that the credit support data comprise credit support amounts of different credit support times.
The further technical scheme is that when the credit risk of the user does not meet the requirement, the risk of the user is determined to be too large, and the adjustment of the loan interest rate is not carried out.
The further technical scheme is that the adjustment strategy of the loan interest rate of the user comprises whether adjustment of the loan interest rate and adjustment level of the loan interest rate are needed.
The further technical scheme is that the adjustment strategy of the loan interest rate of the user is determined by the limit use frequency of the user and the credit risk of the user, and specifically comprises the following steps:
performing benchmark adjustment level of loan interest rate of the user based on the credit risk of the user, and determining an interest rate adjustment factor matched by the user according to the line usage frequency of the user;
An adjustment policy for the user's loan interest rate is determined by the interest rate adjustment factor and the benchmark adjustment level.
In a second aspect, the present invention provides a differential adjustment system based on data analysis, and the differential adjustment method based on data analysis is characterized by specifically comprising:
The system comprises a user dividing module, a class-one user strategy generating module and a class-two user strategy generating module;
the user dividing module is responsible for determining credit branch data of the user on different credit platforms according to user characteristics of the user, determining credit risks of the user according to the credit branch data, determining user population of the user based on the user characteristics of the user when the credit risks of the user meet requirements, and dividing the user into a class-one target user and a class-two target user based on the user population of the user;
The user policy generation module is responsible for acquiring browsing data of a credit account of the user on a specific credit platform when the user is a target user, and determining an adjustment policy of the loan interest rate of the user by combining the credit risk of the user;
and the second class user policy generation module is responsible for determining the credit limit of the user on a specific credit platform according to the credit limit data of the user on the specific credit platform when the user is the second class target user, determining the credit limit frequency of the user according to the credit limit of the user on different credit limit times, and determining the adjustment policy of the loan interest rate of the user according to the credit limit frequency of the user and the credit risk of the user when the user is the adjustable user according to the credit limit frequency of the user.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention as set forth hereinafter.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings;
FIG. 1 is a flow chart of a differential adjustment method based on data analysis;
FIG. 2 is a flow chart of a method of determining a credit risk for a user;
FIG. 3 is a flow chart of a method of determining an adjustment policy for loan interest rates for a class of target users when the user is the user;
FIG. 4 is a flow chart of a method of determining a user's credit utilization frequency;
FIG. 5 is a block diagram of a differential conditioning system based on data analysis.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
The adjustment of loan interest rate is an important means for improving the utilization rate of funds by a consuming financial institution, but in the prior art, the dynamic adjustment of the adjustment modes of loan interest rates of different users is not considered according to the credit risk and the credit utilization frequency of the users, so that unnecessary property loss cannot be avoided, the utilization rate of funds cannot be improved, and the property income rate of the consuming financial institution is improved.
In order to solve the technical problems, according to whether a user has a credit borrowing condition on a specific credit platform, the credit borrowing condition can be divided into a class of target users and a class of target users, for the class of target users without credit borrowing records, the adjustment strategy of the loan interest rate is determined through the browsing condition and the credit risk of the credit account of the user, and for the class of target users with the credit borrowing records, the adjustment strategy of the loan interest rate is determined through the use condition and the credit risk of the credit account of the user, so that the balance of the property interest rate and the safety of a consuming financial institution is realized.
Specifically, the application adopts the following technical scheme:
And determining the credit risk of the user according to the credit support data of the user on different credit platforms, specifically determining the credit risk of the user according to the overdue times of the credit support, and when the credit risk is smaller, taking the user without the credit support record on the specific credit platform as a type of target user and taking the user with the credit support record on the specific credit platform as a type of target user.
Determining an adjustment strategy of the loan interest rate of the user according to the browsing data of the user on the credit account of the specific credit platform and the credit risk of the user, specifically determining the loan demand of the user according to the browsing times, determining an adjustment evaluation value of the user according to the weight of the loan demand and the credit risk, and determining whether the adjustment of the loan interest rate of the user is required according to the adjustment evaluation value;
The credit limit of the user is determined according to the credit limit of the specific credit platform and the credit limit of different credit limit times, specifically, the credit limit of the user can be determined according to the product of the duty ratio of the credit limit of different credit limit times and the credit limit of the credit limit times, when the credit limit of the user is larger, the user is determined to be an adjustable user, and whether the adjustment of the loan interest rate of the user can be carried out is determined according to the credit limit of the user, the weight of the credit risk of the user and the weight of the credit risk of the user.
Further explanation will be made below from two perspectives of the method class embodiment and the system class embodiment.
In order to solve the above-mentioned problems, according to one aspect of the present invention, as shown in fig. 1, there is provided a differential adjustment method based on data analysis, which is characterized by comprising:
Determining credit branch data of a user on different credit platforms according to user characteristics of the user, determining credit risk of the user according to the credit branch data, determining a user population of the user based on the user characteristics of the user when the credit risk of the user meets requirements, and dividing the user into a class-one target user and a class-two target user based on the user population of the user;
Further, the credit support data includes credit support times and support amounts of different credit support times.
In one possible embodiment, as shown in fig. 2, the method for determining the credit risk of the user in the above step is as follows:
Taking a credit platform with credit limit of the user as an associated credit platform, and determining basic credit risk of the user according to the number of the associated credit platforms of the user and the credit limit of different associated credit platforms;
Determining the credit count of the user in different dividing time periods and the credit limit of the different credit count through the credit count data of the user in different associated credit platforms, and determining the credit count risk of the user in different dividing time periods according to the credit count of the user in different dividing time periods and the credit limit of the different credit count;
The credit risk of the user is determined based on the credit branch risk of the user over different divided time periods and the base credit risk of the user.
Further, the method for determining the credit support risk comprises the following steps:
determining the risk of the user's credit frequency according to the credit count, and determining the risk of the user's credit limit according to the credit limit of different credit count;
And determining credit support risks of the user in different dividing time periods based on the weight sum of the support credit limit risks and the support frequency risks.
Specifically, when the credit risk of the user does not meet the requirement, determining that the risk of the user is too large, and temporarily not adjusting the loan interest rate.
In a further possible embodiment, the method of determining the credit risk of the user in the above steps is:
s11, taking a credit platform with credit line as an associated credit platform, determining basic credit risks of the user according to the number of the associated credit platforms of the user and the credit line of different associated credit platforms, judging whether the basic credit risks of the user meet requirements, if so, entering a next step, and if not, determining that the credit risks of the user do not meet the requirements;
S12, determining the credit support times of the user in different dividing time periods and the support line of the different credit support times through the credit support data of the user in different associated credit platforms, determining the credit support risks of the user in different dividing time periods according to the credit support times of the user in different dividing time periods and the support line of the different credit support times, judging whether the credit support risks do not meet the dividing time periods of the requirements, if yes, entering the next step, and if no, entering the step S15;
S13, taking a divided time period of which the credit risk does not meet the requirement as a risk time period, judging whether the number of the risk time periods meets the requirement, if so, entering the next step, and if not, determining that the credit risk of the user does not meet the requirement;
S14, determining weight values of different risk time periods based on time intervals of the different risk time periods from the current time, determining comprehensive support risks by combining credit support risks of the different risk time periods and the number of the risk time periods, judging whether the comprehensive support risks meet requirements, if so, entering the next step, and if not, determining that the credit risks of the users do not meet the requirements;
S15, determining weight values of different dividing time periods according to time intervals of the different dividing time periods from the current moment, and determining credit risks of the user according to credit support risks of the user in the different dividing time periods and basic credit risks of the user.
In a further possible embodiment, the method of determining the credit risk of the user in the above steps is:
Taking a credit platform with credit line existence of the user as an associated credit platform, determining basic credit risk of the user according to the number of the associated credit platforms of the user and the credit line existence of different associated credit platforms, determining that the credit risk of the user meets the requirement when the basic credit risk of the user is smaller than a preset risk value, and determining the credit risk of the user through the basic credit risk of the user;
When the basic credit risk of the user is in a preset risk interval, determining the credit count number of the user in different dividing time periods and the credit limit of the different credit count numbers through the credit limit data of the user in different associated credit platforms, and determining the credit count risk of the user in different dividing time periods according to the credit count number of the user in different dividing time periods and the credit limit of the different credit count numbers;
Taking a divided time period of which the credit risk does not meet the requirement as a risk time period, judging whether the number of the risk time periods meets the requirement, if so, entering the next step, and if not, determining that the credit risk of the user does not meet the requirement;
Determining weight values of different risk time periods based on time intervals of the different risk time periods from the current time, determining comprehensive branch risks by combining credit branch risks of the different risk time periods and the number of the risk time periods, judging whether the comprehensive branch risks meet requirements, if so, entering the next step, and if not, determining that the credit risks of the users do not meet the requirements;
Determining weight values of different dividing time periods according to time intervals of the different dividing time periods from the current moment, and determining credit risks of the user by combining credit branch risks of the user in the different dividing time periods and basic credit risks of the user;
and when the technical credit risk of the user is not in the preset risk interval and is not smaller than the preset risk value, determining that the credit risk of the user does not meet the requirement.
The determining the user population of the user based on the user characteristics of the user specifically includes:
Determining whether the user has a credit record in a specific credit platform according to the user characteristics of the user, and dividing the user into different user groups according to whether the user has the credit record in the specific credit platform.
Further, when the user does not have the credit count record in the specific credit platform, the user is determined to be a type-target user, and when the user has the credit count record in the specific credit platform, the user is determined to be a type-target user.
When the user is a target user, acquiring browsing data of a credit account of the user on a specific credit platform, and determining an adjustment strategy of the loan interest rate of the user by combining the credit risk of the user;
Specifically, the browsing data includes browsing times, browsing time of different browsing times and browsing duration.
In one possible embodiment, as shown in fig. 3, when the user is a target user, the method for determining the adjustment policy of the loan interest rate of the user in the above steps is:
determining a benchmark adjustment level of loan interest rate for the user from the user's credit risk;
determining the browsing times of the user based on the browsing data of the credit account of the user on a specific credit platform, and determining the loan requirement assessment of the user by combining the time intervals and the browsing time lengths of different browsing times from the current moment;
and determining an adjustment strategy of the loan interest rate of the user by using the loan requirement evaluation value of the user and the benchmark adjustment level of the loan interest rate of the user.
Further, the adjustment policy of the loan interest rate of the user comprises whether adjustment of the loan interest rate and adjustment level of the loan interest rate are needed.
Specifically, the adjustment level of the loan interest rate is determined according to the reference loan interest rate of the central row and the preset up-down floating level.
In another possible embodiment, when the user is a target user, the method for determining the adjustment policy of the loan interest rate of the user in the above steps is:
Determining the browsing times of the user in preset time based on the browsing data of the credit account of the user in the specific credit platform, and determining the loan requirement assessment of the user in the preset time by combining the time intervals of the browsing times of different browsing times in the preset time from the current moment and the browsing time length;
determining the browsing times of the user based on the browsing data of the credit account of the user on a specific credit platform, and determining the loan requirement assessment of the user by combining the time intervals and the browsing time lengths of different browsing times from the current moment;
When the loan demand evaluation value of the user and the loan demand evaluation value in the preset time do not meet the requirements, determining to temporarily not adjust the loan interest rate;
when any one of the loan requirement assessment amount of the user within a preset time meets the requirement:
determining a benchmark adjustment level of loan interest rate for the user from the user's credit risk;
When the loan requirement assessment amount of the user in the preset time meets the requirement:
Determining an adjustment strategy of the loan interest rate of the user through the loan demand evaluation value of the user, the loan demand evaluation value of the user in a preset time and a reference adjustment level of the loan interest rate of the user;
When the loan requirement evaluation value of the user in the preset time does not meet the requirement: a determination of an adjustment policy for the user's loan interest rate is made based on the benchmark adjustment hierarchy.
In another possible embodiment, when the user is a target user, the method for determining the adjustment policy of the loan interest rate of the user in the above steps is:
s21, determining the browsing times of the user based on the browsing data of the credit account of the user on a specific credit platform, judging whether the browsing times of the user meet the requirement, if so, entering a step S23, and if not, entering a next step;
s22, determining the browsing times of the user in preset time through the different browsing times of the user, judging whether the browsing times of the user in the preset time meet the requirements, if so, entering the next step, and if not, determining that adjustment of loan interest rate is not carried out;
s23, determining a reference adjustment level of the loan interest rate of the user through the credit risk of the user, judging whether the browsing times of the user and the browsing times in the preset time are both in a corresponding browsing time interval, if so, determining an adjustment strategy of the loan interest rate of the user based on the reference adjustment level, and if not, entering the next step;
S24, determining browsing times of the user in preset time based on browsing data of a credit account of the user in a specific credit platform, determining loan demand assessment amount of the user in preset time by combining time intervals of different browsing times in the preset time from the current moment and browsing time length, judging whether the loan demand assessment amount of the user in the preset time is larger than a preset demand value, if so, entering a next step, and if not, determining a loan interest rate adjustment strategy of the user based on the reference adjustment level;
S25, determining the browsing times of the user based on the browsing data of the credit account of the user on the specific credit platform, determining the loan demand evaluation value of the user by combining the time interval and the browsing duration of the browsing time of different browsing times from the current moment, and determining the adjustment strategy of the loan interest rate of the user by combining the loan demand evaluation value of the user in the preset time and the reference adjustment level of the loan interest rate of the user.
When the user is a class-II target user, the credit count number of the user on a specific credit platform is determined through the credit count data of the user on the specific credit platform, the credit limit of the user is determined by combining the credit limit of the user on different credit count numbers, and when the user is determined to be an adjustable user based on the credit limit of the user, the adjustment strategy of the loan interest rate of the user is determined through the credit limit of the user and the credit risk of the user.
In one possible embodiment, as shown in fig. 4, the method for determining the credit support frequency of the user in the above step is:
determining the frequency evaluation quantity of different credit branch times by using the interval time of the different credit branch times of the user on a specific credit platform and the adjacent credit branch times and the credit line of the credit branch times;
Acquiring average interval time among different credit count times of the user and the credit count times with the interval time smaller than a preset time interval, and determining the credit limit count frequency of the user by combining the frequency evaluation quantity of the different credit count times of the user and the credit count times of the user.
Further, when the limit usage frequency of the user is greater than a preset frequency limit, it is determined that the user does not belong to the adjustable user.
In another possible embodiment, the method for determining the credit frequent degree of the user in the above step is:
Acquiring the credit count of the user on a designated credit platform, judging whether the credit count of the user on the designated credit platform is larger than a preset count, if so, determining that the user does not belong to an adjustable user, and if not, entering the next step;
Taking the credit count with the credit limit larger than the preset credit limit as screening count, judging whether the screening count of the user on a designated credit platform is smaller than the preset screening count, if so, entering the next step, and if not, determining that the user does not belong to an adjustable user;
Determining the frequency evaluation amount of different credit support times by using the interval time of the different credit support times and the adjacent credit support times of the user on a specific credit platform and the support line of the credit support times, judging whether the credit support times which do not meet the requirement of the frequency evaluation amount are in a preset support time interval, if so, entering the next step, and if not, determining that the user does not belong to an adjustable user;
Acquiring average interval time among different credit count times of the user and the credit count times with the interval time smaller than a preset time interval, and determining the credit limit count frequency of the user by combining the frequency evaluation quantity of the different credit count times of the user and the credit count times of the user.
In another possible embodiment, the method for determining the credit frequent degree of the user in the above step is:
acquiring the total credit limit of the user in different dividing time periods, and when the dividing time period that the total credit limit is larger than a preset limit threshold exists:
Taking a dividing time period with the total credit limit larger than a preset limit threshold value as a screening dividing time period, and determining that the user does not belong to an adjustable user when the number of the screening dividing time periods does not meet the requirement;
When the number of screening and dividing time periods meets the requirement, acquiring the credit count of the user on a designated credit platform, taking the credit count with the credit limit larger than a preset credit limit as the screening count, judging whether the screening count of the user on the designated credit platform is smaller than the preset screening count, if so, entering the next step, and if not, determining that the user does not belong to an adjustable user;
Determining the frequency evaluation amount of different credit support times by using the interval time of the different credit support times and the adjacent credit support times of the user on a specific credit platform and the support line of the credit support times, judging whether the credit support times which do not meet the requirement of the frequency evaluation amount are in a preset support time interval, if so, entering the next step, and if not, determining that the user does not belong to an adjustable user;
acquiring average interval time among different credit count times of the user and the credit count times with the interval time smaller than a preset time interval, and determining the credit limit count frequency of the user by combining the frequency evaluation quantity of the different credit count times of the user and the credit count times of the user;
When no divided time period exists for which the total credit limit is greater than a preset limit threshold value: the user is determined to be an adjustable user and the credit line usage frequency of the user is determined by the average value of the total credit line in different dividing time periods.
It should be noted that, the adjustment policy for determining the loan interest rate of the user according to the credit limit usage frequency of the user and the credit risk of the user specifically includes:
performing benchmark adjustment level of loan interest rate of the user based on the credit risk of the user, and determining an interest rate adjustment factor matched by the user according to the line usage frequency of the user;
An adjustment policy for the user's loan interest rate is determined by the interest rate adjustment factor and the benchmark adjustment level.
On the other hand, as shown in fig. 5, the present invention provides a differential adjustment system based on data analysis, and the differential adjustment method based on data analysis is characterized by comprising:
The system comprises a user dividing module, a class-one user strategy generating module and a class-two user strategy generating module;
the user dividing module is responsible for determining credit branch data of the user on different credit platforms according to user characteristics of the user, determining credit risks of the user according to the credit branch data, determining user population of the user based on the user characteristics of the user when the credit risks of the user meet requirements, and dividing the user into a class-one target user and a class-two target user based on the user population of the user;
The user policy generation module is responsible for acquiring browsing data of a credit account of the user on a specific credit platform when the user is a target user, and determining an adjustment policy of the loan interest rate of the user by combining the credit risk of the user;
and the second class user policy generation module is responsible for determining the credit limit of the user on a specific credit platform according to the credit limit data of the user on the specific credit platform when the user is the second class target user, determining the credit limit frequency of the user according to the credit limit of the user on different credit limit times, and determining the adjustment policy of the loan interest rate of the user according to the credit limit frequency of the user and the credit risk of the user when the user is the adjustable user according to the credit limit frequency of the user.
Through the above embodiments, the present invention has the following beneficial effects:
1. The method has the advantages that the users are divided into one type of target users and two types of target users based on the user population of the users, the division of the user population is realized from the perspective of user characteristics, the difference of the demand for interest rate adjustment caused by the fact that whether the users have credit account use difference is fully considered, and the influence of interest rate adjustment on the benefits of the credit platform is reduced on the basis of guaranteeing the use effectiveness of the allowance funds of the credit platform.
2. The credit risk of the user is singly considered by utilizing the browsing data of the credit account and the adjustment strategy of the loan interest rate of the credit risk determination user, and the screening of the user with higher demand for the use of the credit account is realized by further combining the browsing data of the credit account of the user, so that a foundation is laid for the targeted generation of the differentiated adjustment strategy of the loan interest rate.
3. The adjustment strategy of the loan interest rate of the user is determined through the credit limit utilization frequency of the user and the credit risk, so that the classified adjustment of the user from the angles of the credit risk and the credit limit utilization frequency is realized, the reliable screening of the user with less credit limit utilization or less credit limit utilization times is ensured, the interest rate adjustment is carried out on the user in a targeted manner, and the fund benefit of a credit platform is improved.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (11)
1. The differential adjusting method based on data analysis is characterized by comprising the following steps of:
Determining credit branch data of a user on different credit platforms according to user characteristics of the user, determining credit risk of the user according to the credit branch data, determining a user population of the user based on the user characteristics of the user when the credit risk of the user meets requirements, and dividing the user into a class-one target user and a class-two target user based on the user population of the user;
When the user is a target user, acquiring browsing data of a credit account of the user on a specific credit platform, and determining an adjustment strategy of the loan interest rate of the user by combining the credit risk of the user;
When the user is a class-II target user, the credit count number of the user on a specific credit platform is determined through the credit count data of the user on the specific credit platform, the credit limit of the user is determined by combining the credit limit of the user on different credit count numbers, and when the user is determined to be an adjustable user based on the credit limit of the user, the adjustment strategy of the loan interest rate of the user is determined through the credit limit of the user and the credit risk of the user.
2. The differential adjustment method based on data analysis according to claim 1, wherein the credit support data includes credit support times and credit line amounts of different credit support times.
3. A method of differential adjustment based on data analysis according to claim 1, characterized in that the method of determination of the credit risk of the user is:
Taking a credit platform with credit limit of the user as an associated credit platform, and determining basic credit risk of the user according to the number of the associated credit platforms of the user and the credit limit of different associated credit platforms;
Determining the credit count of the user in different dividing time periods and the credit limit of the different credit count through the credit count data of the user in different associated credit platforms, and determining the credit count risk of the user in different dividing time periods according to the credit count of the user in different dividing time periods and the credit limit of the different credit count;
The credit risk of the user is determined based on the credit branch risk of the user over different divided time periods and the base credit risk of the user.
4. A method of differential adjustment based on data analysis according to claim 3, characterized in that the method of determination of the credit support risk is:
determining the risk of the user's credit frequency according to the credit count, and determining the risk of the user's credit limit according to the credit limit of different credit count;
And determining credit support risks of the user in different dividing time periods based on the weight sum of the support credit limit risks and the support frequency risks.
5. The differential adjustment method based on data analysis according to claim 1, wherein when the credit risk of the user does not meet the requirement, the risk of the user is determined to be excessive, and adjustment of loan interest rate is temporarily not performed.
6. The differential adjustment method based on data analysis according to claim 1, wherein the determining of the user population of the user based on the user characteristics of the user, in particular comprises:
Determining whether the user has a credit record in a specific credit platform according to the user characteristics of the user, and dividing the user into different user groups according to whether the user has the credit record in the specific credit platform.
7. The differential adjustment method based on data analysis according to claim 1, wherein when the user does not have a credit record for a credit on a specific credit platform, the user is determined to be a class-one target user, and when the user has a credit record for a credit on a specific credit platform, the user is determined to be a class-two target user.
8. The differential adjustment method based on data analysis according to claim 1, wherein when the user is a type of target user, the method for determining the adjustment policy of the loan interest rate of the user is as follows:
determining a benchmark adjustment level of loan interest rate for the user from the user's credit risk;
determining the browsing times of the user based on the browsing data of the credit account of the user on a specific credit platform, and determining the loan requirement assessment of the user by combining the time intervals and the browsing time lengths of different browsing times from the current moment;
and determining an adjustment strategy of the loan interest rate of the user by using the loan requirement evaluation value of the user and the benchmark adjustment level of the loan interest rate of the user.
9. The differential adjustment method based on data analysis according to claim 1, wherein the adjustment policy of the loan interest rate of the user includes whether adjustment of the loan interest rate, adjustment level of the loan interest rate is required.
10. The differential adjustment method based on data analysis according to claim 1, wherein the adjustment strategy for the loan interest rate of the user is determined by the amount of credit utilization frequency of the user and the credit risk of the user, specifically comprising:
performing benchmark adjustment level of loan interest rate of the user based on the credit risk of the user, and determining an interest rate adjustment factor matched by the user according to the line usage frequency of the user;
An adjustment policy for the user's loan interest rate is determined by the interest rate adjustment factor and the benchmark adjustment level.
11. A differential adjustment system based on data analysis, which adopts the differential adjustment method based on data analysis as claimed in any one of claims 1 to 10, and is characterized by comprising the following specific steps:
The system comprises a user dividing module, a class-one user strategy generating module and a class-two user strategy generating module;
the user dividing module is responsible for determining credit branch data of the user on different credit platforms according to user characteristics of the user, determining credit risks of the user according to the credit branch data, determining user population of the user based on the user characteristics of the user when the credit risks of the user meet requirements, and dividing the user into a class-one target user and a class-two target user based on the user population of the user;
The user policy generation module is responsible for acquiring browsing data of a credit account of the user on a specific credit platform when the user is a target user, and determining an adjustment policy of the loan interest rate of the user by combining the credit risk of the user;
and the second class user policy generation module is responsible for determining the credit limit of the user on a specific credit platform according to the credit limit data of the user on the specific credit platform when the user is the second class target user, determining the credit limit frequency of the user according to the credit limit of the user on different credit limit times, and determining the adjustment policy of the loan interest rate of the user according to the credit limit frequency of the user and the credit risk of the user when the user is the adjustable user according to the credit limit frequency of the user.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711969A (en) * | 2018-08-17 | 2019-05-03 | 深圳壹账通智能科技有限公司 | Campus credit methods, device, equipment and storage medium based on data analysis |
| CN113379456A (en) * | 2021-06-11 | 2021-09-10 | 重庆农村商业银行股份有限公司 | Credit interest rate differential pricing method, device, equipment and storage medium |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711969A (en) * | 2018-08-17 | 2019-05-03 | 深圳壹账通智能科技有限公司 | Campus credit methods, device, equipment and storage medium based on data analysis |
| CN113379456A (en) * | 2021-06-11 | 2021-09-10 | 重庆农村商业银行股份有限公司 | Credit interest rate differential pricing method, device, equipment and storage medium |
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