CN119067772B - Artificial intelligent automobile financial wind control system based on big data - Google Patents

Artificial intelligent automobile financial wind control system based on big data Download PDF

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CN119067772B
CN119067772B CN202411571309.6A CN202411571309A CN119067772B CN 119067772 B CN119067772 B CN 119067772B CN 202411571309 A CN202411571309 A CN 202411571309A CN 119067772 B CN119067772 B CN 119067772B
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CN119067772A (en
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何易葵
李静
黄翔
何为
高明贤
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Sichuan Silk Road E Buy Technology Co ltd
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Abstract

The invention provides an artificial intelligent automobile financial wind control system based on big data, which relates to the technical field of automobile financial risk monitoring and aims to realize more efficient and highly reliable automobile financial wind control, and comprises a big data grouping module, a data processing module and a data processing module, wherein the big data grouping module is used for dividing all individual objects into a plurality of groups; the system comprises a first data acquisition module, a first data analysis module, a second data acquisition module and a second data analysis module, wherein the first data acquisition module is used for acquiring global automobile financial data based on the groups, the first data analysis module is used for identifying local automobile financial risks based on a first neural network, the second data acquisition module is used for acquiring local automobile financial data aiming at the groups judged to have the local automobile financial risks, and the second data analysis module is used for identifying individual automobile financial risks based on a second neural network. The invention has the advantages of high efficiency and reliability of automobile financial wind control.

Description

Artificial intelligent automobile financial wind control system based on big data
Technical Field
The invention relates to the technical field of automobile financial risk monitoring, in particular to an artificial intelligent automobile financial wind control system based on big data.
Background
With the development of automobiles, sales volume of the automobiles is increased, and automobile financial services are also increased.
Automotive finance encompasses credit services and the like, and therefore there is a financial risk. In order to ensure the steady state of the automobile finance, risk monitoring on the automobile finance is a necessary working link of a service provider. The data collected in the automotive financial field are complex in terms of individuals and data types, and the prior art processes based on such data with significant computational effort. However, the reduction of data processing steps causes inaccurate data monitoring and other problems.
Therefore, there is a need for an improvement in automotive financial management that achieves more efficient and reliable automotive financial management.
Disclosure of Invention
The invention aims to provide an artificial intelligent automobile financial wind control system based on big data, which can realize more efficient and highly reliable automobile financial wind control.
The invention is realized by the following technical scheme:
artificial intelligence car finance wind control system based on big data includes:
the big data grouping module is used for dividing all individual objects into a plurality of groups;
the first data acquisition module is used for acquiring global automobile financial data based on the group;
A first data analysis module for identifying local automotive financial risk based on a first neural network;
The second data acquisition module is used for acquiring local automobile financial data aiming at the group judged to have the local automobile financial risk;
And a second data analysis module for identifying individual automotive financial risks based on the second neural network.
Preferably, the method for dividing all individual objects into a plurality of groups is as follows:
respectively obtaining the total amount and the stage number of the automobile financial loan of the individual object;
respectively acquiring the automotive financial loan risk parameters of the individual objects:
;
Wherein, Is the firstAn automotive financial loan risk parameter for each of the individual subjects,AndRespectively the firstThe total amount and staging number of the automotive financial loan for each of the individual subjects,AndAs a result of the empirical weight of the model,A total number of the individual subjects;
obtaining the group number of the group ;
Respectively obtaining the maximum value of the automotive financial loan risk parameters of the individual objectsAnd minimum valueAt the maximum valueAnd minimum valueEqually split betweenAnd dividing the individual objects falling into the same interval into the same group.
Preferably, the number of the acquisition groupsThe method of (1) is as follows:
;
;
;
Wherein, As a function of the natural index of refraction,AndAre all the parameters in the middle of the method,AndRespectively a function of taking a maximum value and taking a minimum value,As a round-up function.
Preferably, the method for collecting global car financial data based on the group comprises the following steps:
and respectively collecting the total amount of the residual credit, the total amount of the theoretical repayment, the total amount of the actual repayment, the number of vehicle accidents and the loss amount of the vehicle accidents in each month in the continuous M months of the individual subjects of each group.
Preferably, the method for identifying local automotive financial risk based on the first neural network comprises the following steps:
transmitting the global automotive financial data to an input layer;
Respectively carrying out feature extraction on each group through a feature extraction layer to obtain group feature parameters;
Carrying out data nonlinear processing through at least one hidden layer;
and outputting whether each group has local automobile financial risk or not through an output layer.
Preferably, the method for extracting the characteristics of each group through the characteristic extraction layer comprises the following steps:
;
;
Wherein, AndRespectively the firstA first characteristic parameter and a second characteristic parameter of each group,、AndRespectively represent the firstThe group of consecutive M monthsThe total amount of remaining credits, the total amount of theoretical payouts and the total amount of actual payouts for one month,AndRespectively the firstThe group of consecutive M monthsThe number of vehicle accidents and the amount of vehicle accident loss for one month,Is an intermediate parameter;
combining the first characteristic parameter and the second characteristic parameter:
;
Wherein, Is the firstThe group characteristic parameters of the groups.
Preferably, the hidden layer uses a ReLU activation function, and the output layer uses a Sigmoid activation function.
Preferably, the method for collecting local car financial data for the group judged to have local car financial risk is as follows:
a total amount of remaining credits, a total amount of theoretical payouts, a total amount of actual payouts, a number of vehicle accidents, and a vehicle accident loss amount for each month of the continuous M months for each of the individual subjects in the group identified as having a localized automotive financial risk is collected.
Preferably, the second neural network is identical in structure to the first neural network.
The technical scheme of the invention has at least the following advantages and beneficial effects:
according to the invention, the automobile financial wind control is performed based on big data and artificial intelligence, so that comprehensive risk assessment can be performed, and potential risk factors can be identified;
The invention carries out grouping processing on the data, carries out advanced global judgment and then local judgment, has different risks and requirements facing individuals with different credit amounts, can provide more personalized financial service and management strategy after grouping, simultaneously reduces the calculation burden of the system and improves the calculation efficiency;
The data grouping of the invention has more pertinence, can be divided into different groups according to different data distribution conditions based on credit amount, has more data grouping for data distribution which is more uneven, not only considers the limitation of the group number, but also considers the problem of data density, and is beneficial to improving the reliability of global analysis;
The local analysis can adopt the same model as the global analysis, so that the training cost is further saved, and the calculation force resource is optimized.
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Fig. 1 is a schematic diagram of an artificial intelligent automobile financial wind control system based on big data according to embodiment 1 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1
The embodiment provides an artificial intelligent automobile financial wind control system based on big data, referring to fig. 1, including:
the big data grouping module is used for dividing all individual objects into a plurality of groups;
the first data acquisition module is used for acquiring global automobile financial data based on the group;
A first data analysis module for identifying local automotive financial risk based on a first neural network;
The second data acquisition module is used for acquiring local automobile financial data aiming at the group judged to have the local automobile financial risk;
And a second data analysis module for identifying individual automotive financial risks based on the second neural network.
In this embodiment, global recognition is performed through grouping, which is equivalent to that data of a group is regarded as an individual to perform judgment, and detailed recognition judgment is performed on the individual after the group with risk is recognized, so that the method is beneficial to effectively reducing the power consumption, improving the processing efficiency, and simultaneously guaranteeing the reliability of judgment. Can intelligent high-efficient to car finance risk monitor, prevention finance problem takes place. The combination of big data and artificial intelligence provides data support for decision making, can make more scientific judgment in complex market environment,
In this embodiment, the method for dividing all individual objects into a plurality of groups is as follows:
respectively obtaining the total amount and the stage number of the automobile financial loan of the individual object;
respectively acquiring the automotive financial loan risk parameters of the individual objects:
;
Wherein, Is the firstAn automotive financial loan risk parameter for each of the individual subjects,AndRespectively the firstThe total amount and staging number of the automotive financial loan for each of the individual subjects,AndAs a result of the empirical weight of the model,A total number of the individual subjects;
it is specifically noted that, AndThe setting of (2) can be determined according to the practical application scene by grouping more importance on the total loan or the loan of each periodAndIs allocated to the size of the (c).
Obtaining the group number of the group;
Respectively obtaining the maximum value of the automotive financial loan risk parameters of the individual objectsAnd minimum valueAt the maximum valueAnd minimum valueEqually split betweenAnd dividing the individual objects falling into the same interval into the same group.
Further, the acquisition group numberThe method of (1) is as follows:
;
;
;
Wherein, As a function of the natural index of refraction,AndAre all the parameters in the middle of the method,AndRespectively a function of taking a maximum value and taking a minimum value,As a round-up function.
The group number of the embodiment considers the total amount and the stage number of the finance loan of all individual objects, so as to obtain the risk parameters of the automobile finance loan, and the group is set according to the uniform distribution condition of the risk parameters of the automobile finance loan. For even data groups, the number of the groups can be increased due to the fact that partial backlog of a large amount of data occurs, and for data with uneven distribution, the problems of feature loss and the like caused by excessive data of one group are prevented.
As a preferred scheme, the method for collecting global car financial data based on the group comprises the following steps:
and respectively collecting the total amount of the residual credit, the total amount of the theoretical repayment, the total amount of the actual repayment, the number of vehicle accidents and the loss amount of the vehicle accidents in each month in the continuous M months of the individual subjects of each group.
Next, the method of identifying local automotive financial risk based on the first neural network may include:
transmitting the global automotive financial data to an input layer;
Respectively carrying out feature extraction on each group through a feature extraction layer to obtain group feature parameters;
Carrying out data nonlinear processing through at least one hidden layer;
and outputting whether each group has local automobile financial risk or not through an output layer.
Specifically, the method for extracting the features of each group by the feature extraction layer is preferably as follows:
;
;
Wherein, AndRespectively the firstA first characteristic parameter and a second characteristic parameter of each group,、AndRespectively represent the firstThe group of consecutive M monthsThe total amount of remaining credits, the total amount of theoretical payouts and the total amount of actual payouts for one month,AndRespectively the firstThe group of consecutive M monthsThe number of vehicle accidents and the amount of vehicle accident loss for one month,Is an intermediate parameter;
combining the first characteristic parameter and the second characteristic parameter:
;
Wherein, Is the firstThe group characteristic parameters of the groups.
In a specific setting, the hidden layer preferably adopts a ReLU activation function, and the output layer can adopt a Sigmoid activation function to perform classification tasks.
In global recognition, it is equivalent to integrating data of one group together as one individual to make a preliminary determination. The first characteristic parameter and the second characteristic parameter capture trend characteristics of average data of duration through a sliding window, which is equivalent to acquiring data comprehensive characteristics of a plurality of different continuous periods, wherein the first characteristic parameter can extract risks of repayment conditions, and the second characteristic parameter can advance risks brought by fault consumption.
Then, the method for collecting the local car financial data aiming at the group judged to have the local car financial risk comprises the following steps:
a total amount of remaining credits, a total amount of theoretical payouts, a total amount of actual payouts, a number of vehicle accidents, and a vehicle accident loss amount for each month of the continuous M months for each of the individual subjects in the group identified as having a localized automotive financial risk is collected.
To reduce the training burden, the second neural network structure is preferably the same as the first neural network.
Since global recognition is equivalent to regarding a group as an individual object, the same neural network can be applied to directly perform feature capturing and judging of the same mode on the data of the individual object during specific local recognition.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1.基于大数据的人工智能汽车金融风控系统，其特征在于，包括：1. An artificial intelligence automobile financial risk control system based on big data, characterized by including: 大数据分组模块，用于将所有个体对象划分为多个组别；A big data grouping module is used to divide all individual objects into multiple groups; 第一数据采集模块，用于基于所述组别采集全局汽车金融数据；A first data collection module, used for collecting global automobile financial data based on the groups; 第一数据分析模块，用于基于第一神经网络识别局部汽车金融风险；A first data analysis module, used for identifying local automobile financial risks based on a first neural network; 第二数据采集模块，用于针对判断为具备局部汽车金融风险的组别采集局部汽车金融数据；A second data collection module is used to collect local automobile financial data for groups judged to have local automobile financial risks; 第二数据分析模块，用于基于第二神经网络识别个体汽车金融风险；A second data analysis module, for identifying individual automobile financial risks based on a second neural network; 所述将所有个体对象划分为多个组别的方法为：The method of dividing all individual objects into multiple groups is: 分别获取所述个体对象的汽车金融贷款总数额和分期数；Respectively obtain the total amount and number of installments of the automobile financial loan of the individual object; 分别获取所述个体对象的汽车金融贷款风险参数：Get the auto finance loan risk parameters of the individual objects respectively: ； ; 其中，为第个所述个体对象的汽车金融贷款风险参数，和分别为第个所述个体对象的汽车金融贷款总数额和分期数，和为经验权重, 为所述个体对象的总数；in, For the The auto finance loan risk parameters of the individual object, and Respectively The total amount and number of installments of the automobile finance loan for each individual subject, and is the experience weight, is the total number of individual subjects; 获取组别的组数；Get the number of groups ; 分别获取所述个体对象的汽车金融贷款风险参数的最大值和最小值，在最大值和最小值之间等分出个区间，落入相同区间的所述个体对象划分到同一个组别；Get the maximum value of the auto finance loan risk parameter of the individual object respectively and minimum value , at the maximum value and minimum value Divide equally between intervals, the individual subjects falling into the same interval are classified into the same group; 所述获取组别的组数的方法为：The number of groups to obtain The method is: ; ; ; ; ； ; 其中，为自然指数函数，和均为中间参数，和分别为取最大值和取最小值的函数，为向上取整函数；in, is the natural exponential function, and These are all intermediate parameters. and are the functions for taking the maximum and minimum values respectively, is the ceiling rounding function; 所述基于所述组别采集全局汽车金融数据的方法为：The method for collecting global automobile financial data based on the group is: 分别采集每个组别的所述个体对象的连续M个月中每个月的剩余贷款数总额、理论还款数总额、实际还款数总额、车辆事故数量和车辆事故损失金额；Collect the total amount of remaining loans, the total amount of theoretical repayments, the total amount of actual repayments, the number of vehicle accidents and the amount of vehicle accident losses of the individual subjects in each group for each of the consecutive M months; 所述基于第一神经网络识别局部汽车金融风险的方法包括：The method for identifying local automobile financial risks based on the first neural network includes: 将所述全局汽车金融数据传输到输入层；Transmitting the global automobile financial data to the input layer; 通过特征提取层分别对每个组别进行特征提取得到组别特征参数；Through the feature extraction layer, feature extraction is performed on each group to obtain group feature parameters; 通过至少一个隐藏层进行数据非线性化处理；Performing nonlinear processing of data through at least one hidden layer; 通过输出层分别输出每个组别是否存在局部汽车金融风险；Through the output layer, each group is outputted to determine whether there is a local automobile financial risk; 通过特征提取层分别对每个组别进行特征提取的方法为：The method of extracting features from each group through the feature extraction layer is: ; ; ; ; 其中，和分别为第个组别的第一特征参数和第二特征参数，、和分别代表第个组别的连续M个月中第个月的剩余贷款数总额、理论还款数总额和实际还款数总额，和分别为第个组别的连续M个月中第个月的车辆事故数量和车辆事故损失金额，为中间参数；in, and Respectively The first characteristic parameter and the second characteristic parameter of each group, , and Respectively represent The first The total amount of remaining loans, theoretical repayments and actual repayments for the month. and Respectively The first The number of vehicle accidents and the amount of vehicle accident losses in each month, is the intermediate parameter; 组合所述第一特征参数和所述第二特征参数：Combining the first characteristic parameter and the second characteristic parameter: ; ; 其中，为第个组别的所述组别特征参数。in, For the The group characteristic parameters of each group. 2.根据权利要求1所述的基于大数据的人工智能汽车金融风控系统，其特征在于，所述隐藏层采用ReLU激活函数，所述输出层采用Sigmoid激活函数。2. The big data-based artificial intelligence automobile financial risk control system according to claim 1 is characterized in that the hidden layer adopts a ReLU activation function and the output layer adopts a Sigmoid activation function. 3.根据权利要求2所述的基于大数据的人工智能汽车金融风控系统，其特征在于，所述针对判断为具备局部汽车金融风险的组别采集局部汽车金融数据的方法为：3. The big data-based artificial intelligence automobile financial risk control system according to claim 2 is characterized in that the method for collecting local automobile financial data for the group judged to have local automobile financial risks is: 采集被识别为存在局部汽车金融风险的所述组别中的每个所述个体对象的连续M个月中每个月的剩余贷款数总额、理论还款数总额、实际还款数总额、车辆事故数量和车辆事故损失金额。The total amount of remaining loans, the total amount of theoretical repayments, the total amount of actual repayments, the number of vehicle accidents and the amount of vehicle accident losses for each of the individual subjects in the group identified as having local automobile financial risks in each of the consecutive M months are collected. 4.根据权利要求3所述的基于大数据的人工智能汽车金融风控系统，其特征在于，所述第二神经网络结构与所述第一神经网络相同。4. The big data-based artificial intelligence automobile financial risk control system according to claim 3 is characterized in that the second neural network structure is the same as the first neural network.
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