Automobile financial risk control method and system based on big data and AI

Abstract

The invention relates to the technical field of automobile finance risk control operation management, and particularly discloses an automobile finance risk control method and system based on big data and AI. Obtaining a risk management value of each type of risk management data of automobile finance at the current moment; based on the risk management values of all types of risk management data of the automobile finance at all moments within a preset time period before the current moment, obtaining an overall risk control quantity value of the automobile finance at the current moment; and obtaining an automobile finance risk control result at the current moment based on the total risk control quantity value of the automobile finance at the current moment. According to the method, the credit risk aspect, the loan risk aspect, the market risk aspect and the associated institution risk aspect of the automobile finance are integrated, the overall risk degree of the automobile finance is accurately quantified, and the effect and efficiency of automobile finance risk management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile financial risk control operation management, and in particular to an automobile financial risk control method and system based on big data and AI. Background Art

[0002] At present, with the rapid development of the auto finance market, auto finance risk control faces an increasingly complex and volatile risk environment. These risks include but are not limited to credit risk, loan risk, market risk and related institution risk. Effective management and quantification of these risks are essential to maintaining financial stability, protecting the interests of investors and promoting the sustainable development of the auto finance industry. First, credit risk in the auto finance industry refers to the risk that the borrower or debtor fails to fulfill the repayment obligation as agreed. This is directly related to the asset quality and profitability of financial institutions (such as banks and other institutions). Secondly, loan risk mainly involves the probability of default and default loss rate of the loan portfolio. With the diversification of auto finance products, the management of loan risk has become more complicated. Furthermore, market risk stems from the fluctuation of the use of auto finance collateral (vehicles), which may have a great impact on the value of auto finance collateral (vehicles), and it cannot be ensured that the collateral can cover the loan balance when the customer defaults. Finally, the risk of related institutions involves the risk status of other institutions that have business dealings or interest relations with auto finance institutions. Once these related institutions have problems, they may cause direct or indirect financial losses to auto finance institutions. Therefore, how to comprehensively consider the credit risk, loan risk, market risk, and affiliated institution risk of auto finance to accurately quantify the overall risk level of auto finance has become the most critical issue in auto finance at present.

[0003] However, the existing automobile finance risk control method and system based on big data and AI only analyzes the risk of financial loans of vehicles by judging the quality of vehicles, improves the operational control efficiency of automobile finance, and analyzes the credit review process to determine whether the credit review process is qualified, and prevents the risk of corresponding automobile finance transactions caused by poor credit review accuracy. However, this only considers a few risk aspects of automobile finance, and does not comprehensively consider the credit risk, loan risk, market risk, and risk of related institutions of automobile finance to accurately quantify the overall risk level of automobile finance. For example, the patent with the publication number of "CN114971876A" and the name of the patent is "A kind of automobile finance risk control operation management system based on big data", and its method includes the following steps: analyzing the quality of the vehicles to be sold, and analyzing the risk of financial loans of the corresponding vehicles by judging the quality of the vehicles, thereby improving the operational control efficiency of automobile finance, reducing the risk of financial loans, and improving the cooperation efficiency of borrowers and lenders; analyzing the credit review process to determine whether the credit review process is qualified, and preventing the risk of corresponding automobile finance transactions caused by poor credit review accuracy, thereby reducing the control efficiency of automobile finance operations, affecting the transaction security of borrowers and lenders in automobile finance, and thus affecting the transaction success rate of automobile finance. However, this patent only analyzes the risk of financial loans for vehicles by judging vehicle quality, thereby improving the operational control efficiency of auto finance, and analyzing the credit review process to determine whether the credit review process is qualified, thereby preventing risks in corresponding auto finance transactions caused by poor credit review accuracy. However, this only takes into account a few risk aspects of auto finance, and does not comprehensively consider the credit risk, loan risk, market risk, and affiliated institution risk of auto finance to accurately quantify the overall risk level of auto finance.

[0004] Therefore, the present invention proposes an automobile financial risk control method and system based on big data and AI. Summary of the invention

[0005] The present invention provides an automobile finance risk control method and system based on big data and AI, which are used to accurately obtain the risk management value of each type of risk management data of automobile finance at the current moment according to the first financial risk judgment result at the current moment and each type of risk management data of automobile finance at each moment in a preset time period before the current moment, so as to quantify the risk degree of automobile finance at the current moment on each type of risk management data, obtain the risk management upper limit threshold, risk management lower limit threshold and risk management matrix of automobile finance at the current moment according to the risk management values ​​of all types of risk management data of automobile finance at all moments in the preset time period before the current moment, and then accurately obtain the overall risk control value of automobile finance at the current moment, so as to quantify the overall risk degree of automobile finance at the current moment, accurately obtain the second financial risk judgment result at the current moment according to the overall risk control value of automobile finance at the current moment, and then obtain the automobile finance risk control result at the current moment, so as to integrate the credit risk, loan risk, market risk and related institution risk of automobile finance, and accurately quantify the overall risk degree of automobile finance, which is helpful to improve the effect and efficiency of automobile finance risk management and reduce potential risk losses.

[0006] The present invention provides an automobile financial risk control method based on big data and AI, comprising:

[0007] S1: based on all risk management data of automobile finance at each moment in a preset time period before the current moment, obtain a first financial risk judgment result at the current moment;

[0008] S2: If the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology, obtain the risk management value of each type of risk management data of automobile finance at the current moment;

[0009] S3: based on the risk management values ​​of all risk management data of automobile finance at all times in a preset time period before the current moment, obtain the risk management upper threshold and the risk management lower threshold of automobile finance at the current moment, and based on the risk management values ​​of all risk management data of automobile finance at all times in the preset time period before the current moment, obtain the risk management matrix of automobile finance at the current moment, and based on the risk management upper threshold, the risk management lower threshold and the risk management matrix of automobile finance at the current moment, obtain the overall risk control value of automobile finance at the current moment;

[0010] S4: Based on the overall risk control value of automobile finance at the current moment, obtain the second financial risk judgment result at the current moment, and based on the first financial risk judgment result and the second financial risk judgment result at the current moment, obtain the automobile finance risk control result at the current moment.

[0011] Preferably, the automobile financial risk control method based on big data and AI, S1: based on all risk management data of automobile finance at each moment in a preset time period before the current moment, obtaining a first financial risk judgment result at the current moment, including:

[0012] Obtain all risk management data of auto finance at each moment in the preset time period before the current moment, where all risk management data include credit risk management data, loan risk management data, market risk management data, and risk management data of affiliated institutions, and credit risk management data includes personal credit record information, personal work income information, and personal debt situation, loan risk management data includes loan issuance process information, market risk management data includes vehicle mileage, vehicle maintenance information, and vehicle insurance amount, and affiliated institution risk management data includes affiliated institution performance record and affiliated institution financial status;

[0013] Based on all types of risk management data of automobile finance at all times within a preset time period before the current moment, a first financial risk judgment result at the current moment is obtained.

[0014] Preferably, the automobile financial risk control method based on big data and AI obtains the first financial risk judgment result at the current moment based on all risk management data of automobile finance at all moments in a preset time period before the current moment, including:

[0015] Determine whether there is an overdue payment record in the personal credit record information of the credit risk management data of automobile finance at all times in the preset time period before the current time. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time;

[0016] Determine whether there is any illegal operation in the loan issuance process information of the loan risk management data of automobile finance at all times in the preset time period before the current time. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time;

[0017] Determine whether the vehicle insurance amount of the market risk management data of automobile finance at all times in the preset time period before the current time is greater than the preset repair amount. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time;

[0018] Determine whether there is a default record in the performance record of the affiliated institution of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current moment. If so, the existence of risk is regarded as the first financial risk judgment result at the current moment; otherwise, the pending is regarded as the first financial risk judgment result at the current moment.

[0019] Preferably, the automobile financial risk control method based on big data and AI, S2: if the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology, obtain the risk management value of each type of risk management data of automobile finance at the current moment, including:

[0020] If the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in the preset time period before the current moment and the scoring model of the corresponding type of risk management data, obtain the risk scoring value of each type of risk management data of automobile finance at each moment in the preset time period before the current moment;

[0021] Based on the risk scoring values ​​of each type of risk management data of automobile finance at all times within a preset time period before the current time, the risk management value of each type of risk management data of automobile finance at the current time is obtained.

[0022] Preferably, the automobile finance risk control method based on big data and AI obtains the risk management value of each type of automobile finance risk management data at the current moment based on the risk scoring value of each type of automobile finance risk management data at all moments in a preset time period before the current moment, including:

[0023] The mean and standard deviation of the risk score values ​​of each type of risk management data of automobile finance at all times in the preset time period before the current time are used as the reference mean and reference standard deviation of the corresponding type of risk management data of automobile finance at the current time;

[0024] Determine whether the risk score value of each type of risk management data of automobile finance at the current moment is greater than the reference mean value of the corresponding type of risk management data of automobile finance at the current moment. If so, the risk score value of each type of risk management data of automobile finance at the current moment is used as the risk management value of the corresponding type of risk management data of automobile finance at the current moment. Otherwise, the sum of the risk score value of each type of risk management data of automobile finance at the current moment and the reference standard deviation is used as the risk management value of the corresponding type of risk management data of automobile finance at the current moment.

[0025] Preferably, the automobile finance risk control method based on big data and AI obtains the upper and lower thresholds of the risk management of automobile finance at the current moment based on the risk management values ​​of all risk management data of automobile finance at all moments in a preset time period before the current moment, including:

[0026]

[0027]

[0028] Among them, β 1is the upper threshold of risk management for auto finance at the current moment, β 2 is the lower limit threshold of auto finance risk management at the current moment, Q is the mean of the risk management values ​​of auto finance credit risk management data at all moments in the preset time period before the current moment, σ q is the standard deviation of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current time, q max is the maximum value of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current time, q min is the minimum value of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current moment, E is the mean value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current moment, σ e is the standard deviation of the risk management value of the loan risk management data of auto finance at all times in the preset time period before the current time, e max is the maximum value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current time, e min is the minimum value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current moment, U is the mean value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current moment, σ u is the standard deviation of the risk management value of the market risk management data of auto finance at all times in the preset time period before the current moment, u max is the maximum value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current time, u min is the minimum value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current moment, Z is the mean value of the risk management value of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current moment, σ z is the standard deviation of the risk management value of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current time, z max is the maximum value of the risk management value of the risk management data of the associated institutions of automobile finance at all times in the preset time period before the current time, z min It is the minimum value among the risk management values ​​of the risk management data of the associated institutions of automobile finance at all times in the preset time period before the current time.

[0029] Preferably, the automobile finance risk control method based on big data and AI obtains the risk management matrix of automobile finance at the current moment based on the risk management values ​​of all types of risk management data of automobile finance at all moments in a preset time period before the current moment, including:

[0030]

[0031] Among them, S is the risk management matrix of automobile finance at the current moment, q 1 is the risk management value of the credit risk management data of automobile finance at the first moment in the preset time period before the current moment, e 1 is the risk management value of the loan risk management data of automobile finance at the first moment in the preset time period before the current moment, u 1 is the risk management value of the market risk management data of automobile finance at the first moment in the preset time period before the current moment, z 1 is the risk management value of the risk management data of the associated institutions of automobile finance at the first moment in the preset time period before the current moment, q 2 is the risk management value of the credit risk management data of automobile finance at the second moment in the preset time period before the current moment, e 2 is the risk management value of the loan risk management data of automobile finance at the second moment in the preset time period before the current moment, u 2 is the risk management value of the market risk management data of auto finance at the second moment in the preset time period before the current moment, z 2 is the risk management value of the risk management data of the associated institutions of automobile finance at the second moment in the preset time period before the current moment, q n is the risk management value of the credit risk management data of automobile finance at the nth moment in the preset time period before the current moment, e n is the risk management value of the loan risk management data of automobile finance at the nth moment in the preset time period before the current moment, u n is the risk management value of the market risk management data of automobile finance at the nth moment in the preset time period before the current moment, z n is the risk management value of the risk management data of the associated institution of automobile finance at the nth moment in the preset time period before the current moment, and n is the total number of all moments in the preset time period before the current moment.

[0032] Preferably, the automobile finance risk control method based on big data and AI obtains the overall risk control value of automobile finance at the current moment based on the risk management upper threshold, risk management lower threshold and risk management matrix of automobile finance at the current moment, including:

[0033] Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix element whose matrix element is not greater than the upper threshold of the risk management of automobile finance at the current moment and whose corresponding matrix element is not less than the lower threshold of the risk management of automobile finance at the current moment is regarded as the first statistical matrix element of the corresponding column matrix of the risk management matrix of automobile finance at the current moment;

[0034] Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix elements whose matrix elements are less than the lower limit threshold of the risk management of automobile finance at the current moment are regarded as the second statistical matrix elements of the corresponding column matrix of the risk management matrix of automobile finance at the current moment;

[0035] Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix element whose matrix element is greater than the upper threshold of the risk management of automobile finance at the current moment is regarded as the third statistical matrix element of the corresponding column matrix of the risk management matrix of automobile finance at the current moment;

[0036] Based on the first statistical matrix element, the second statistical matrix element and the third statistical matrix element of all column matrices of the risk management matrix of automobile finance at the current moment, the overall risk control value of automobile finance at the current moment is obtained, which is:

[0037]

[0038] Among them, θ is the overall risk control value of automobile finance at the current moment, ρ is the mean value of all matrix elements of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, and m 1 is the total number of elements in the first statistical matrix of all column matrices of the risk management matrix of automobile finance at the current moment, m 2 is the total number of elements in the second statistical matrix of all column matrices of the risk management matrix of automobile finance at the current moment, m 3 is the total number of elements in the third statistical matrix of all column matrices of the risk management matrix of automobile finance at the current moment, p 1 is the number of the first statistical matrix elements of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, p 2 is the number of elements of the second statistical matrix of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, p 3 is the number of elements of the third statistical matrix of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0039] Preferably, the automobile financial risk control method based on big data and AI, S4: based on the overall risk control value of automobile finance at the current moment, obtaining the second financial risk judgment result at the current moment, and based on the first financial risk judgment result and the second financial risk judgment result at the current moment, obtaining the automobile financial risk control result at the current moment, including:

[0040] Determine whether the overall risk control value of automobile finance at the current moment is greater than a preset overall risk control value threshold; if so, consider the existence of risk as the second financial risk judgment result at the current moment; otherwise, consider no risk as the second financial risk judgment result at the current moment;

[0041] If the first financial risk judgment result or the second financial risk judgment result at the current moment indicates that there is a risk, an alarm is sent to the system background.

[0042] The present invention provides an automobile financial risk control system based on big data and AI, which is used to execute any one of the automobile financial risk control methods based on big data and AI in Embodiments 1 to 9, including:

[0043] A judgment module, used to obtain a first financial risk judgment result at the current moment based on all risk management data of automobile finance at each moment in a preset time period before the current moment;

[0044] A first calculation module is used to obtain the risk management value of each type of risk management data of automobile finance at the current moment based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology if the first financial risk judgment result at the current moment is pending;

[0045] The second calculation module is used to obtain the risk management upper threshold and the risk management lower threshold of the automobile finance at the current moment based on the risk management value of all risk management data of the automobile finance at all moments in the preset time period before the current moment, and obtain the risk management matrix of the automobile finance at the current moment based on the risk management value of all risk management data of the automobile finance at all moments in the preset time period before the current moment, and obtain the overall risk control value of the automobile finance at the current moment based on the risk management upper threshold, the risk management lower threshold and the risk management matrix of the automobile finance at the current moment;

[0046] The risk control alarm module is used to obtain the second financial risk judgment result at the current moment based on the overall risk control value of automobile finance at the current moment, and to obtain the automobile financial risk control result at the current moment based on the first financial risk judgment result and the second financial risk judgment result at the current moment.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows: according to the first financial risk judgment result at the current moment and each type of risk management data of automobile finance at each moment in a preset time period before the current moment, the risk management value of each type of risk management data of automobile finance at the current moment is accurately obtained, and the risk degree of automobile finance at the current moment on each type of risk management data is quantified; according to the risk management values ​​of all types of risk management data of automobile finance at all moments in the preset time period before the current moment, the risk management upper limit threshold, the risk management lower limit threshold and the risk management matrix of automobile finance at the current moment are obtained, and then the overall risk control value of automobile finance at the current moment is accurately obtained, and the overall risk degree of automobile finance at the current moment is quantified; according to the overall risk control value of automobile finance at the current moment, the second financial risk judgment result at the current moment is accurately obtained, and then the automobile finance risk control result at the current moment is obtained, and the credit risk, loan risk, market risk and related institution risk of comprehensive automobile finance are achieved. Accurate quantification of the overall risk degree of automobile finance is achieved, which is helpful to improve the effect and efficiency of automobile financial risk management and reduce potential risk losses.

[0048] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structure specifically pointed out in the written application document.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 This is a flow chart of an automobile financial risk control method based on big data and AI in an embodiment of the present invention;

[0052] Figure 2 The figure is a schematic diagram of an automobile financial risk control system based on big data and AI in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] Embodiment 1:

[0055] The present invention provides an automobile financial risk control method based on big data and AI, referring to Figure 1 ,include:

[0056] S1: based on all risk management data of automobile finance at each moment in a preset time period before the current moment, obtain a first financial risk judgment result at the current moment;

[0057] S2: If the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology, obtain the risk management value of each type of risk management data of automobile finance at the current moment;

[0058] S3: based on the risk management values ​​of all risk management data of automobile finance at all times in a preset time period before the current moment, obtain the risk management upper threshold and the risk management lower threshold of automobile finance at the current moment, and based on the risk management values ​​of all risk management data of automobile finance at all times in the preset time period before the current moment, obtain the risk management matrix of automobile finance at the current moment, and based on the risk management upper threshold, the risk management lower threshold and the risk management matrix of automobile finance at the current moment, obtain the overall risk control value of automobile finance at the current moment;

[0059] S4: Based on the overall risk control value of automobile finance at the current moment, obtain the second financial risk judgment result at the current moment, and based on the first financial risk judgment result and the second financial risk judgment result at the current moment, obtain the automobile finance risk control result at the current moment.

[0060] In this embodiment, the preset time period is a preset time period for obtaining all types of risk management data of automobile finance, such as 30 days.

[0061] In this embodiment, each moment is a moment selected from a preset time period before the current moment, wherein the time length between each adjacent moment is the same (eg, 1 day), and the current moment is the selected moment.

[0062] In this embodiment, the first financial risk judgment result at the current moment is an initial judgment result on whether there is a potential risk in the automobile financial business at the current moment, based on all types of risk management data of automobile finance at each moment in a preset time period before the current moment, and the first financial risk judgment result at the current moment includes pending and existing risks.

[0063] In this embodiment, the AI ​​technology is the existing neural network model technology.

[0064] In this embodiment, the risk management value of each type of risk management data of automobile finance at the current moment is a numerical value obtained based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology, which can truly reflect the risk level of automobile finance at the current moment in this category of risk management data.

[0065] In this embodiment, the risk management upper limit threshold of automobile finance at the current moment is obtained based on the risk management value of all types of risk management data of automobile finance at all moments in a preset time period before the current moment, and is used as the threshold value for dividing the serious and minor risk conditions of automobile finance in all risk management categories at the current moment.

[0066] In this embodiment, the lower limit threshold of risk management of automobile finance at the current moment is obtained based on the risk management value of all types of risk management data of automobile finance at all moments in a preset time period before the current moment, and is used as the threshold for dividing the risk status of automobile finance at the current moment in all risk management categories between slight and no risk.

[0067] In this embodiment, the risk management matrix of automobile finance at the current moment is a matrix obtained by using the risk management values ​​of all types of risk management data of automobile finance at all moments in a preset time period before the current moment as matrix elements, and is used to analyze the overall risk situation of automobile finance at the current moment.

[0068] In this embodiment, the overall risk control value of automobile finance at the current moment is a value obtained based on the risk management upper threshold, risk management lower threshold and risk management matrix of automobile finance at the current moment, which can represent the overall risk level of automobile finance at the current moment.

[0069] In this embodiment, the second financial risk judgment result at the current moment is a secondary judgment result on whether there is a potential risk in the automobile financial business at the current moment, which is obtained based on the overall risk control value of automobile finance at the current moment, and the second financial risk judgment result at the current moment includes no risk and risk.

[0070] In this embodiment, the automobile financial risk control result at the current moment is a result of whether the system background sends an alarm based on the first financial risk judgment result and the second financial risk judgment result at the current moment.

[0071] The beneficial effects of the above technology are as follows: according to the first financial risk judgment result at the current moment and each type of risk management data of automobile finance at each moment in the preset time period before the current moment, the risk management value of each type of risk management data of automobile finance at the current moment is accurately obtained, and the risk degree of automobile finance at the current moment in each type of risk management data is quantified; according to the risk management values ​​of all types of risk management data of automobile finance at all moments in the preset time period before the current moment, the risk management upper limit threshold, risk management lower limit threshold and risk management matrix of automobile finance at the current moment are obtained, and then the overall risk control value of automobile finance at the current moment is accurately obtained, and the overall risk degree of automobile finance at the current moment is quantified; according to the overall risk control value of automobile finance at the current moment, the second financial risk judgment result at the current moment is accurately obtained, and then the automobile finance risk control result at the current moment is obtained, and the credit risk, loan risk, market risk and related institution risk of comprehensive automobile finance are realized. Accurate quantification of the overall risk degree of automobile finance is achieved, which helps to improve the effect and efficiency of automobile financial risk management and reduce potential risk losses.

[0072] Embodiment 2:

[0073] Based on Example 1, the automobile financial risk control method based on big data and AI, S1: based on all risk management data of automobile finance at each moment in a preset time period before the current moment, obtain the first financial risk judgment result at the current moment, including:

[0074] Obtain all risk management data of auto finance at each moment in the preset time period before the current moment, where all risk management data include credit risk management data, loan risk management data, market risk management data, and risk management data of affiliated institutions, and credit risk management data includes personal credit record information, personal work income information, and personal debt situation, loan risk management data includes loan issuance process information, market risk management data includes vehicle mileage, vehicle maintenance information, and vehicle insurance amount, and affiliated institution risk management data includes affiliated institution performance record and affiliated institution financial status;

[0075] Based on all types of risk management data of automobile finance at all times within a preset time period before the current moment, a first financial risk judgment result at the current moment is obtained.

[0076] In this embodiment, the personal credit record information is the personal credit record of the automobile finance loan user at each moment in a preset time period before the current moment.

[0077] In this embodiment, the personal work income information is the amount information of the personal work income of the loan user of the automobile finance at each moment in a preset time period before the current moment.

[0078] In this embodiment, the personal debt situation is the amount information of the personal debt of the loan user of the automobile finance at each moment in a preset time period before the current moment.

[0079] In this embodiment, the loan issuance process information is a process record of issuing loans to auto finance loan users at each moment in a preset time period before the current moment (the process record of issuing loans to auto finance loan users at each moment is the process of issuing loans most recently to the corresponding moment in time).

[0080] In this embodiment, the vehicle usage mileage is the total mileage of the vehicle (the vehicle purchased by the loan user through auto finance) accumulated by the loan user of the auto finance at each moment in a preset time period before the current moment.

[0081] In this embodiment, the vehicle maintenance information is maintenance information of vehicles purchased by the loan users of automobile finance at each moment in a preset time period before the current moment.

[0082] In this embodiment, the vehicle accident amount is the total amount of accumulated accidents incurred by the loan users of automobile finance when purchasing vehicles at each moment in a preset time period before the current moment.

[0083] In this embodiment, the performance record of the associated institution is a record of the performance of the associated institutions of automobile finance (such as automobile dealers, insurance companies, guarantee companies, etc.) at each moment in a preset time period before the current moment.

[0084] In this embodiment, the financial status of the associated institution is a record of the financial status of the associated institution (such as automobile dealers, insurance companies, guarantee companies, etc.) of automobile finance at each moment in a preset time period before the current moment.

[0085] The beneficial effect of the above technology is: based on all types of risk management data of automobile finance at each moment in the preset time period before the current moment, the first financial risk judgment result at the current moment is obtained, which realizes the initial judgment of whether there is potential risk in the automobile financial business at the current moment, which is convenient for judging whether to carry out subsequent processing.

[0086] Embodiment 3:

[0087] On the basis of Example 2, the automobile financial risk control method based on big data and AI obtains the first financial risk judgment result at the current moment based on all risk management data of automobile finance at all moments in a preset time period before the current moment, including:

[0088] Determine whether there is an overdue payment record in the personal credit record information of the credit risk management data of automobile finance at all times in the preset time period before the current time. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time;

[0089] Determine whether there is any illegal operation in the loan issuance process information of the loan risk management data of automobile finance at all times in the preset time period before the current time. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time;

[0090] Determine whether the vehicle insurance amount of the market risk management data of automobile finance at all times in the preset time period before the current time is greater than the preset repair amount. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time;

[0091] Determine whether there is a default record in the performance record of the affiliated institution of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current moment. If so, the existence of risk is regarded as the first financial risk judgment result at the current moment; otherwise, the pending is regarded as the first financial risk judgment result at the current moment.

[0092] In this embodiment, the overdue payment record is an overdue payment record existing in the personal credit record information.

[0093] In this embodiment, the illegal operation is to determine whether there is any operation that violates the prescribed process in the loan issuance process information.

[0094] In this embodiment, the preset maintenance amount is a threshold value of the maintenance amount that is preset to obtain the first financial risk judgment result at the current moment.

[0095] In this embodiment, the default record is a non-performance record existing in the performance record of the affiliated institution.

[0096] The beneficial effects of the above technology are as follows: This embodiment provides in detail a specific method for obtaining the first financial risk judgment result at the current moment based on all types of risk management data of automobile finance at all moments in a preset time period before the current moment.

[0097] Embodiment 4:

[0098] Based on Example 1, the automobile financial risk control method based on big data and AI, S2: if the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology, obtain the risk management value of each type of risk management data of automobile finance at the current moment, including:

[0099] If the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in the preset time period before the current moment and the scoring model of the corresponding type of risk management data, obtain the risk scoring value of each type of risk management data of automobile finance at each moment in the preset time period before the current moment;

[0100] Based on the risk scoring values ​​of each type of risk management data of automobile finance at all times within a preset time period before the current time, the risk management value of each type of risk management data of automobile finance at the current time is obtained.

[0101] In this embodiment, the scoring model for risk management data uses a large amount of pre-collected risk management data of each type as the model input, and manually annotated risk scores (risk scores) of the corresponding risk areas represented by the corresponding risk management data as the model output. The trained model can input each type of risk management data and output the risk score of the corresponding type of risk management data.

[0102] The beneficial effects of the above technology are: it realizes the scoring of the risk degree of the corresponding risk area represented by each type of risk management data, and then obtains the risk management value of each type of risk management data of automobile finance at the current moment, which facilitates the subsequent determination of the upper and lower risk management thresholds.

[0103] Embodiment 5:

[0104] On the basis of Example 4, the automobile finance risk control method based on big data and AI obtains the risk management value of each type of automobile finance risk management data at the current moment based on the risk scoring value of each type of automobile finance risk management data at all times in a preset time period before the current moment, including:

[0105] The mean and standard deviation of the risk score values ​​of each type of risk management data of automobile finance at all times in the preset time period before the current time are used as the reference mean and reference standard deviation of the corresponding type of risk management data of automobile finance at the current time;

[0106] Determine whether the risk score value of each type of risk management data of automobile finance at the current moment is greater than the reference mean value of the corresponding type of risk management data of automobile finance at the current moment. If so, the risk score value of each type of risk management data of automobile finance at the current moment is used as the risk management value of the corresponding type of risk management data of automobile finance at the current moment. Otherwise, the sum of the risk score value of each type of risk management data of automobile finance at the current moment and the reference standard deviation is used as the risk management value of the corresponding type of risk management data of automobile finance at the current moment.

[0107] The beneficial effects of the above technology are: according to the risk scoring value of each type of risk management data of automobile finance at all times in a preset time period before the current moment, a reference mean and a reference standard deviation of each type of risk management data of automobile finance at the current moment are obtained, and then according to the reference mean and the reference standard deviation of each type of risk management data of automobile finance at the current moment, a risk management value of each type of risk management data of automobile finance at the current moment is obtained. This embodiment gives in detail a specific method for obtaining the risk management value of each type of risk management data of automobile finance at the current moment based on the risk scoring value of each type of risk management data of automobile finance at all times in a preset time period before the current moment.

[0108] Embodiment 6:

[0109] On the basis of Example 1, the automobile finance risk control method based on big data and AI obtains the risk management upper threshold and the risk management lower threshold of automobile finance at the current moment based on the risk management values ​​of all types of risk management data of automobile finance at all moments in a preset time period before the current moment, including:

[0110]

[0111]

[0112] Among them, β 1 is the upper threshold of risk management for auto finance at the current moment, β 2 is the lower limit threshold of auto finance risk management at the current moment, Q is the mean of the risk management values ​​of auto finance credit risk management data at all moments in the preset time period before the current moment, σ q is the standard deviation of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current time, q max is the maximum value of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current time, q min is the minimum value of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current moment, E is the mean value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current moment, σe is the standard deviation of the risk management value of the loan risk management data of auto finance at all times in the preset time period before the current time, e max is the maximum value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current time, e min is the minimum value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current moment, U is the mean value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current moment, σ u is the standard deviation of the risk management value of the market risk management data of auto finance at all times in the preset time period before the current moment, u max is the maximum value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current time, u min is the minimum value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current moment, Z is the mean value of the risk management value of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current moment, σ z is the standard deviation of the risk management value of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current time, z max is the maximum value of the risk management value of the risk management data of the associated institutions of automobile finance at all times in the preset time period before the current time, z min It is the minimum value among the risk management values ​​of the risk management data of the associated institutions of automobile finance at all times in the preset time period before the current time.

[0113] The beneficial effects of the above technology are as follows: this embodiment gives in detail a risk management value based on all types of risk management data of automobile finance at all times within a preset time period before the current moment, and a specific method for obtaining the risk management upper threshold and the risk management lower threshold of automobile finance at the current moment, and accurately obtains the thresholds used as the dividing line between serious and minor risk conditions, and the dividing line between minor and no risk in all risk management categories of automobile finance at the current moment.

[0114] Embodiment 7:

[0115] On the basis of Example 1, the automobile finance risk control method based on big data and AI obtains the risk management matrix of automobile finance at the current moment based on the risk management values ​​of all types of risk management data of automobile finance at all moments in a preset time period before the current moment, including:

[0116]

[0117] Among them, S is the risk management matrix of automobile finance at the current moment, q 1is the risk management value of the credit risk management data of automobile finance at the first moment in the preset time period before the current moment, e 1 is the risk management value of the loan risk management data of automobile finance at the first moment in the preset time period before the current moment, u 1 is the risk management value of the market risk management data of automobile finance at the first moment in the preset time period before the current moment, z 1 is the risk management value of the risk management data of the associated institutions of automobile finance at the first moment in the preset time period before the current moment, q 2 is the risk management value of the credit risk management data of automobile finance at the second moment in the preset time period before the current moment, e 2 is the risk management value of the loan risk management data of automobile finance at the second moment in the preset time period before the current moment, u 2 is the risk management value of the market risk management data of auto finance at the second moment in the preset time period before the current moment, z 2 is the risk management value of the risk management data of the associated institutions of automobile finance at the second moment in the preset time period before the current moment, q n is the risk management value of the credit risk management data of automobile finance at the nth moment in the preset time period before the current moment, e n is the risk management value of the loan risk management data of automobile finance at the nth moment in the preset time period before the current moment, u n is the risk management value of the market risk management data of automobile finance at the nth moment in the preset time period before the current moment, z n is the risk management value of the risk management data of the associated institution of automobile finance at the nth moment in the preset time period before the current moment, and n is the total number of all moments in the preset time period before the current moment.

[0118] The beneficial effect of the above technology is: based on the risk management values ​​of all types of risk management data of automobile finance at all times in the preset time period before the current moment, the risk management matrix of automobile finance at the current moment is obtained, which is convenient for the subsequent calculation of the overall risk control value of automobile finance at the current moment.

[0119] Embodiment 8:

[0120] On the basis of Example 1, the automobile finance risk control method based on big data and AI obtains the overall risk control value of automobile finance at the current moment based on the risk management upper threshold, risk management lower threshold and risk management matrix of automobile finance at the current moment, including:

[0121] Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix element whose matrix element is not greater than the upper threshold of the risk management of automobile finance at the current moment and whose corresponding matrix element is not less than the lower threshold of the risk management of automobile finance at the current moment is regarded as the first statistical matrix element of the corresponding column matrix of the risk management matrix of automobile finance at the current moment;

[0122] Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix elements whose matrix elements are less than the lower limit threshold of the risk management of automobile finance at the current moment are regarded as the second statistical matrix elements of the corresponding column matrix of the risk management matrix of automobile finance at the current moment;

[0123] Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix element whose matrix element is greater than the upper threshold of the risk management of automobile finance at the current moment is regarded as the third statistical matrix element of the corresponding column matrix of the risk management matrix of automobile finance at the current moment;

[0124] Based on the first statistical matrix element, the second statistical matrix element and the third statistical matrix element of all column matrices of the risk management matrix of automobile finance at the current moment, the overall risk control value of automobile finance at the current moment is obtained, which is:

[0125]

[0126] Among them, θ is the overall risk control value of automobile finance at the current moment, ρ is the mean value of all matrix elements of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, and m 1 is the total number of elements in the first statistical matrix of all column matrices of the risk management matrix of automobile finance at the current moment, m 2 is the total number of elements in the second statistical matrix of all column matrices of the risk management matrix of automobile finance at the current moment, m 3 is the total number of elements in the third statistical matrix of all column matrices of the risk management matrix of automobile finance at the current moment, p 1 is the number of the first statistical matrix elements of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, p 2 is the number of elements of the second statistical matrix of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, p 3 is the number of elements of the third statistical matrix of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0127] The beneficial effects of the above technology are: based on the risk management upper threshold, risk management lower threshold and risk management matrix of automobile finance at the current moment, the overall risk control value of automobile finance at the current moment is accurately obtained, and the overall risk level of automobile finance at the current moment is quantified, which is convenient for determining the subsequent second financial risk judgment result.

[0128] Embodiment 9:

[0129] Based on Example 1, the automobile financial risk control method based on big data and AI, S4: based on the overall risk control value of automobile finance at the current moment, obtaining the second financial risk judgment result at the current moment, and based on the first financial risk judgment result and the second financial risk judgment result at the current moment, obtaining the automobile financial risk control result at the current moment, including:

[0130] Determine whether the overall risk control value of automobile finance at the current moment is greater than a preset overall risk control value threshold; if so, consider the existence of risk as the second financial risk judgment result at the current moment; otherwise, consider no risk as the second financial risk judgment result at the current moment;

[0131] If the first financial risk judgment result or the second financial risk judgment result at the current moment indicates that there is a risk, an alarm is sent to the system background.

[0132] In this embodiment, the preset overall risk control value threshold is a threshold of the overall risk control value that is preset to obtain the second financial risk judgment result at the current moment.

[0133] In this embodiment, the system background is the computer background of the user who uses the automobile financial risk control system based on big data and AI to carry out automobile financial risk control early warning.

[0134] The beneficial effects of the above technology are: based on the overall risk control value of automobile finance at the current moment and the preset overall risk control value threshold, the second financial risk judgment result at the current moment is accurately obtained, and then the automobile financial risk control result at the current moment is obtained, which helps to improve the effectiveness and efficiency of automobile financial risk management and reduce potential risk losses.

[0135] Embodiment 10:

[0136] The present invention provides an automobile financial risk control system based on big data and AI, which is used to execute any one of the automobile financial risk control methods based on big data and AI in embodiments 1 to 9, with reference to Figure 2 ,include:

[0137] A judgment module, used to obtain a first financial risk judgment result at the current moment based on all risk management data of automobile finance at each moment in a preset time period before the current moment;

[0138] A first calculation module is used to obtain the risk management value of each type of risk management data of automobile finance at the current moment based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology if the first financial risk judgment result at the current moment is pending;

[0139] The second calculation module is used to obtain the risk management upper threshold and the risk management lower threshold of the automobile finance at the current moment based on the risk management value of all risk management data of the automobile finance at all moments in the preset time period before the current moment, and obtain the risk management matrix of the automobile finance at the current moment based on the risk management value of all risk management data of the automobile finance at all moments in the preset time period before the current moment, and obtain the overall risk control value of the automobile finance at the current moment based on the risk management upper threshold, the risk management lower threshold and the risk management matrix of the automobile finance at the current moment;

[0140] The risk control alarm module is used to obtain the second financial risk judgment result at the current moment based on the overall risk control value of automobile finance at the current moment, and to obtain the automobile financial risk control result at the current moment based on the first financial risk judgment result and the second financial risk judgment result at the current moment.

[0141] The beneficial effects of the above technology are as follows: according to the first financial risk judgment result at the current moment and each type of risk management data of automobile finance at each moment in the preset time period before the current moment, the risk management value of each type of risk management data of automobile finance at the current moment is accurately obtained, and the risk degree of automobile finance at the current moment in each type of risk management data is quantified; according to the risk management values ​​of all types of risk management data of automobile finance at all moments in the preset time period before the current moment, the risk management upper limit threshold, risk management lower limit threshold and risk management matrix of automobile finance at the current moment are obtained, and then the overall risk control value of automobile finance at the current moment is accurately obtained, and the overall risk degree of automobile finance at the current moment is quantified; according to the overall risk control value of automobile finance at the current moment, the second financial risk judgment result at the current moment is accurately obtained, and then the automobile finance risk control result at the current moment is obtained, and the credit risk, loan risk, market risk and related institution risk of comprehensive automobile finance are realized. Accurate quantification of the overall risk degree of automobile finance is achieved, which helps to improve the effect and efficiency of automobile financial risk management and reduce potential risk losses.

[0142] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention, and the present invention is also intended to include these changes and modifications.

Claims

1. A method for automobile financial risk control based on big data and AI, characterized in that: include: S1: based on all risk management data of automobile finance at each moment in a preset time period before the current moment, obtain a first financial risk judgment result at the current moment; S2: If the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology, obtain the risk management value of each type of risk management data of automobile finance at the current moment; S3: based on the risk management values ​​of all risk management data of automobile finance at all times in a preset time period before the current moment, obtain the risk management upper threshold and the risk management lower threshold of automobile finance at the current moment, and based on the risk management values ​​of all risk management data of automobile finance at all times in the preset time period before the current moment, obtain the risk management matrix of automobile finance at the current moment, and based on the risk management upper threshold, the risk management lower threshold and the risk management matrix of automobile finance at the current moment, obtain the overall risk control value of automobile finance at the current moment; S4: Based on the overall risk control value of automobile finance at the current moment, obtain the second financial risk judgment result at the current moment, and based on the first financial risk judgment result and the second financial risk judgment result at the current moment, obtain the automobile finance risk control result at the current moment.

2. The automobile financial risk control method based on big data and AI according to claim 1, characterized in that: S1: Based on all risk management data of automobile finance at each moment in a preset time period before the current moment, a first financial risk judgment result at the current moment is obtained, including: Obtain all risk management data of auto finance at each moment in the preset time period before the current moment, where all risk management data include credit risk management data, loan risk management data, market risk management data, and risk management data of affiliated institutions, and credit risk management data includes personal credit record information, personal work income information, and personal debt situation, loan risk management data includes loan issuance process information, market risk management data includes vehicle mileage, vehicle maintenance information, and vehicle insurance amount, and affiliated institution risk management data includes affiliated institution performance record and affiliated institution financial status; Based on all types of risk management data of automobile finance at all times within a preset time period before the current moment, a first financial risk judgment result at the current moment is obtained.

3. The automobile financial risk control method based on big data and AI according to claim 2 is characterized in that: Based on all risk management data of automobile finance at all times in a preset time period before the current time, the first financial risk judgment result at the current time is obtained, including: Determine whether there is an overdue payment record in the personal credit record information of the credit risk management data of automobile finance at all times in the preset time period before the current time. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time; Determine whether there is any illegal operation in the loan issuance process information of the loan risk management data of automobile finance at all times in the preset time period before the current time. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time; Determine whether the vehicle insurance amount of the market risk management data of automobile finance at all times in the preset time period before the current time is greater than the preset repair amount. If so, the existence of risk is regarded as the first financial risk judgment result at the current time; otherwise, the pending is regarded as the first financial risk judgment result at the current time; Determine whether there is a default record in the performance record of the affiliated institution of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current moment. If so, the existence of risk is regarded as the first financial risk judgment result at the current moment; otherwise, the pending is regarded as the first financial risk judgment result at the current moment.

4. The automobile financial risk control method based on big data and AI according to claim 1, characterized in that: S2: If the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in the preset time period before the current moment and AI technology, the risk management value of each type of risk management data of automobile finance at the current moment is obtained, including: If the first financial risk judgment result at the current moment is pending, based on each type of risk management data of automobile finance at each moment in the preset time period before the current moment and the scoring model of the corresponding type of risk management data, obtain the risk scoring value of each type of risk management data of automobile finance at each moment in the preset time period before the current moment; Based on the risk scoring values ​​of each type of risk management data of automobile finance at all times within a preset time period before the current time, the risk management value of each type of risk management data of automobile finance at the current time is obtained.

5. The automobile financial risk control method based on big data and AI according to claim 4 is characterized in that: Based on the risk scoring values ​​of each type of risk management data of automobile finance at all times in a preset time period before the current time, the risk management value of each type of risk management data of automobile finance at the current time is obtained, including: The mean and standard deviation of the risk score values ​​of each type of risk management data of automobile finance at all times in the preset time period before the current time are used as the reference mean and reference standard deviation of the corresponding type of risk management data of automobile finance at the current time; Determine whether the risk score value of each type of risk management data of automobile finance at the current moment is greater than the reference mean value of the corresponding type of risk management data of automobile finance at the current moment. If so, the risk score value of each type of risk management data of automobile finance at the current moment is used as the risk management value of the corresponding type of risk management data of automobile finance at the current moment. Otherwise, the sum of the risk score value of each type of risk management data of automobile finance at the current moment and the reference standard deviation is used as the risk management value of the corresponding type of risk management data of automobile finance at the current moment.

6. The automobile financial risk control method based on big data and AI according to claim 1, characterized in that: Based on the risk management values ​​of all risk management data of automobile finance at all times in a preset time period before the current time, the upper and lower thresholds of risk management of automobile finance at the current time are obtained, including: Among them, β1 is the upper threshold of risk management of automobile finance at the current moment, β2 is the lower threshold of risk management of automobile finance at the current moment, Q is the mean of the risk management value of credit risk management data of automobile finance at all moments in the preset time period before the current moment, σ q is the standard deviation of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current time, q max is the maximum value of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current time, q min is the minimum value of the risk management value of the credit risk management data of automobile finance at all times in the preset time period before the current moment, E is the mean value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current moment, σ e is the standard deviation of the risk management value of the loan risk management data of auto finance at all times in the preset time period before the current time, e max is the maximum value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current time, e min is the minimum value of the risk management value of the loan risk management data of automobile finance at all times in the preset time period before the current moment, U is the mean value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current moment, σ u is the standard deviation of the risk management value of the market risk management data of auto finance at all times in the preset time period before the current moment, u max is the maximum value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current time, u min is the minimum value of the risk management value of the market risk management data of automobile finance at all times in the preset time period before the current moment, Z is the mean value of the risk management value of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current moment, σ z is the standard deviation of the risk management value of the risk management data of the affiliated institutions of automobile finance at all times in the preset time period before the current time, z max is the maximum value of the risk management value of the risk management data of the associated institutions of automobile finance at all times in the preset time period before the current time, z min It is the minimum value among the risk management values ​​of the risk management data of the associated institutions of automobile finance at all times in the preset time period before the current time.

7. The automobile financial risk control method based on big data and AI according to claim 1, characterized in that: Based on the risk management values ​​of all risk management data of automobile finance at all times in the preset time period before the current time, the risk management matrix of automobile finance at the current time is obtained, including: Among them, S is the risk management matrix of automobile finance at the current moment, q1 is the risk management value of the credit risk management data of automobile finance at the first moment in the preset time period before the current moment, e1 is the risk management value of the loan risk management data of automobile finance at the first moment in the preset time period before the current moment, u1 is the risk management value of the market risk management data of automobile finance at the first moment in the preset time period before the current moment, z1 is the risk management value of the risk management data of the affiliated institutions of automobile finance at the first moment in the preset time period before the current moment, q2 is the risk management value of the credit risk management data of automobile finance at the second moment in the preset time period before the current moment, e2 is the risk management value of the loan risk management data of automobile finance at the second moment in the preset time period before the current moment, u2 is the risk management value of the market risk management data of automobile finance at the second moment in the preset time period before the current moment, z2 is the risk management value of the risk management data of the affiliated institutions of automobile finance at the second moment in the preset time period before the current moment, q n is the risk management value of the credit risk management data of automobile finance at the nth moment in the preset time period before the current moment, e n is the risk management value of the loan risk management data of automobile finance at the nth moment in the preset time period before the current moment, u n is the risk management value of the market risk management data of automobile finance at the nth moment in the preset time period before the current moment, z n is the risk management value of the risk management data of the associated institution of automobile finance at the nth moment in the preset time period before the current moment, and n is the total number of all moments in the preset time period before the current moment.

8. The automobile financial risk control method based on big data and AI according to claim 1, characterized in that: Based on the current risk management upper threshold, risk management lower threshold and risk management matrix of automobile finance, the overall risk control value of automobile finance at the current moment is obtained, including: Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix element whose matrix element is not greater than the upper threshold of the risk management of automobile finance at the current moment and whose corresponding matrix element is not less than the lower threshold of the risk management of automobile finance at the current moment is regarded as the first statistical matrix element of the corresponding column matrix of the risk management matrix of automobile finance at the current moment; Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix elements whose matrix elements are less than the lower limit threshold of the risk management of automobile finance at the current moment are regarded as the second statistical matrix elements of the corresponding column matrix of the risk management matrix of automobile finance at the current moment; Among all the matrix elements of each column matrix of the risk management matrix of automobile finance at the current moment, the matrix element whose matrix element is greater than the upper threshold of the risk management of automobile finance at the current moment is regarded as the third statistical matrix element of the corresponding column matrix of the risk management matrix of automobile finance at the current moment; Based on the first statistical matrix element, the second statistical matrix element and the third statistical matrix element of all column matrices of the risk management matrix of automobile finance at the current moment, the overall risk control value of automobile finance at the current moment is obtained, which is: Among them, θ is the overall risk control value of automobile finance at the current moment, ρ is the mean of all matrix elements of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, m1 is the total number of first statistical matrix elements of all column matrices of the risk management matrix of automobile finance at the current moment, m2 is the total number of second statistical matrix elements of all column matrices of the risk management matrix of automobile finance at the current moment, m3 is the total number of third statistical matrix elements of all column matrices of the risk management matrix of automobile finance at the current moment, p1 is the first statistical matrix element number of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, p2 is the second statistical matrix element number of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, p3 is the third statistical matrix element number of the column matrix corresponding to the current moment in the risk management matrix of automobile finance at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.

718.

9. The automobile financial risk control method based on big data and AI according to claim 1, characterized in that: S4: Based on the overall risk control value of automobile finance at the current moment, a second financial risk judgment result at the current moment is obtained, and based on the first financial risk judgment result and the second financial risk judgment result at the current moment, an automobile financial risk control result at the current moment is obtained, including: Determine whether the overall risk control value of automobile finance at the current moment is greater than a preset overall risk control value threshold; if so, consider the existence of risk as the second financial risk judgment result at the current moment; otherwise, consider no risk as the second financial risk judgment result at the current moment; If the first financial risk judgment result or the second financial risk judgment result at the current moment indicates that there is a risk, an alarm is sent to the system background.

10. An automobile financial risk control system based on big data and AI, characterized in that: A method for automobile financial risk control based on big data and AI for executing any one of claims 1 to 9, comprising: A judgment module, used to obtain a first financial risk judgment result at the current moment based on all risk management data of automobile finance at each moment in a preset time period before the current moment; A first calculation module is used to obtain the risk management value of each type of risk management data of automobile finance at the current moment based on each type of risk management data of automobile finance at each moment in a preset time period before the current moment and AI technology if the first financial risk judgment result at the current moment is pending; The second calculation module is used to obtain the risk management upper threshold and the risk management lower threshold of the automobile finance at the current moment based on the risk management value of all risk management data of the automobile finance at all moments in the preset time period before the current moment, and obtain the risk management matrix of the automobile finance at the current moment based on the risk management value of all risk management data of the automobile finance at all moments in the preset time period before the current moment, and obtain the overall risk control value of the automobile finance at the current moment based on the risk management upper threshold, the risk management lower threshold and the risk management matrix of the automobile finance at the current moment; The risk control alarm module is used to obtain the second financial risk judgment result at the current moment based on the overall risk control value of automobile finance at the current moment, and to obtain the automobile financial risk control result at the current moment based on the first financial risk judgment result and the second financial risk judgment result at the current moment.

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