Financial loan risk prediction method and system based on big data

Abstract

The invention relates to the technical field of financial loan risk prediction, in particular to a financial loan risk prediction method and system based on big data, and the method comprises the steps: obtaining historical loan data of a loan platform, the historical loan data comprising a credit identifier of each historical loan customer and a plurality of corresponding data tables, each data table comprises a plurality of data items; calculating a default coefficient of the data item based on the credit identifier of the historical loan customer, and updating the historical loan data according to the default coefficient; establishing a prediction model based on the default coefficient, and training the prediction model by taking the updated historical loan data as a training sample; and obtaining loan data of the current loan customer, inputting the loan data of the current loan customer into the trained prediction model to obtain a prediction result, and sending a risk prompt based on the prediction result. Based on big data analysis and prediction, the default risk of the customer is avoided, and the bad loan rate of the loan platform is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of financial loan risk prediction, and in particular to a financial loan risk prediction method and system based on big data. Background Art

[0002] With the development of internet technology and the online economy, the loan business is also growing rapidly, with a growing number of loan platforms and loan types, such as installment loans and banking institutions. Similarly, the types of people who participate in loans are also diverse, and some loan platforms, driven by profit, inevitably engage in malicious lending.

[0003] Due to the lack of face-to-face communication between lenders and borrowers in online lending, borrowers' credit information is incomplete, making it difficult to accurately assess their willingness and ability to repay, thereby increasing the probability of default and losses. Therefore, how to effectively predict the default risk of online loan users and improve the accuracy and efficiency of user credit scoring has become a critical issue that online lending platforms need to address. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a financial loan risk prediction method and system based on big data to address the deficiencies in the existing technology.

[0005] To achieve the above objectives, the present invention provides a financial loan risk prediction method based on big data, the method comprising:

[0006] Acquiring historical loan data of the loan platform, wherein the historical loan data includes a credit identification of each historical loan customer and a corresponding plurality of data tables, each of the data tables including a plurality of data items;

[0007] Calculating the default coefficient of the data item based on the credit identifier of the historical loan customer, and updating the historical loan data according to the default coefficient;

[0008] Establishing a prediction model based on the default coefficient, and using the updated historical loan data as training samples to train the prediction model;

[0009] Obtain loan data of the current loan customer, input the loan data of the current loan customer into the trained prediction model, obtain a prediction result, and issue a risk warning based on the prediction result.

[0010] The beneficial effects of the present invention are: by dividing historical loan data into multiple data tables, calculating the default coefficient of each data item in the data table, and updating the historical loan data, and then establishing a prediction model, and using the updated data to train the model, based on big data analysis and prediction, a score that can accurately evaluate the credit of the current loan customer is obtained, and the score is used to further determine whether to issue a loan to the customer, and the form of loan issuance is adjusted in time, thereby avoiding the customer's default risk and reducing the non-performing loan rate of the loan platform.

[0011] Preferably, the steps of obtaining historical loan data of the loan platform specifically include:

[0012] Obtaining historical loan data from a loan platform and preprocessing the historical loan data;

[0013] The data table of each historical loan customer includes a basic information attribute table, a bank flow record table, an authentication information statistics table and a loan information record table. The jth data table of the nth historical loan customer is recorded as

[0014] The data items of the basic information attribute table include gender, age, occupation, education level, marital status, and household registration type; the data items of the bank transaction record table include annual transaction times, annual income, and annual expenditure; the data items of the authentication information statistics table include credit authentication, video authentication, mobile phone authentication, Taobao authentication, and household registration authentication; and the data items of the loan information record table include annual loan times and annual loan amount;

[0015] Sort all the data items and record the i-th data item of the n-th historical loan customer as

[0016] Among them, the credit identification includes good, average and poor.

[0017] Preferably, the step of calculating the default coefficient of the data item based on the credit identification of the historical loan customer and updating the historical loan data according to the default coefficient specifically includes:

[0018] Assign values to each of the data items of the historical loan customers based on a preset assignment rule, and calculate the default coefficient of each data item:

[0019]

[0020] in, is the default coefficient of the i-th data item of the n-th historical loan customer, The i-th data item and the The assigned values are equal and the credit identification is a poor quantity; The i-th data item and the The assignment of equal number;

[0021] The default coefficient With the data item Perform association and traverse all the historical loan customers, thereby completing the update of the historical customer loan data.

[0022] Preferably, the step of establishing a prediction model based on the default coefficient specifically includes:

[0023] averaging the default coefficients corresponding to the data items in the data table to obtain the default score of the data table, and denoting the default scores of the data tables as y1, y2, y3, and y4;

[0024] Build a prediction model based on the default score:

[0025]

[0026] P is the credit score, that is, the prediction result of the prediction model, B is the basic score, β is the ratio of the number of historical loan customers with poor credit identification to the number of historical loan customers with good credit identification, and a, b, c, and d are the scale factors of each data table.

[0027] Preferably, the step of issuing a risk warning based on the prediction result specifically includes:

[0028] When the credit score is less than a preset first credit threshold, the credit identification of the current loan customer is determined to be poor, and an early warning prompt is issued;

[0029] When the credit score is between the first credit threshold and a preset second credit threshold, the credit indicator of the current loan customer is determined to be average, the loan amount and loan term of the current loan customer are reduced, and a safety reminder is issued;

[0030] When the credit score is greater than the second credit threshold, the credit identification of the current loan customer is determined to be good, and a safety prompt is issued.

[0031] To achieve the above objectives, the present invention further proposes a financial loan risk prediction system based on big data, the system comprising:

[0032] An acquisition module is used to acquire historical loan data of the loan platform, wherein the historical loan data includes the credit identification of each historical loan customer and a corresponding plurality of data tables, each of the data tables including a plurality of data items;

[0033] a calculation module, configured to calculate a default coefficient of the data item based on the credit identifier of the historical loan customer, and update the historical loan data according to the default coefficient;

[0034] A training module, configured to establish a prediction model based on the default coefficient, and train the prediction model using the updated historical loan data as training samples;

[0035] The prediction module is used to obtain the loan data of the current loan customer, input the loan data of the current loan customer into the trained prediction model, obtain the prediction result, and issue a risk warning based on the prediction result.

[0036] Preferably, the acquisition module is specifically used to:

[0037] Obtaining historical loan data from a loan platform and preprocessing the historical loan data;

[0038] The data table of each historical loan customer includes a basic information attribute table, a bank flow record table, an authentication information statistics table and a loan information record table. The jth data table of the nth historical loan customer is recorded as

[0039] The data items of the basic information attribute table include gender, age, occupation, education level, marital status, and household registration type; the data items of the bank transaction record table include annual transaction times, annual income, and annual expenditure; the data items of the authentication information statistics table include credit authentication, video authentication, mobile phone authentication, Taobao authentication, and household registration authentication; and the data items of the loan information record table include annual loan times and annual loan amount;

[0040] Sort all the data items and record the i-th data item of the n-th historical loan customer as

[0041] Among them, the credit identification includes good, average and poor.

[0042] Furthermore, the calculation module is specifically used to:

[0043] Assign values to each of the data items of the historical loan customers based on a preset assignment rule, and calculate the default coefficient of each data item:

[0044]

[0045] in, is the default coefficient of the i-th data item of the n-th historical loan customer, The i-th data item and the The assigned values are equal and the credit identification is a poor quantity; The i-th data item and the The assignment of equal number;

[0046] The default coefficient With the data item Perform association and traverse all the historical loan customers, thereby completing the update of the historical customer loan data.

[0047] Preferably, the training module is specifically used for:

[0048] averaging the default coefficients corresponding to the data items in the data table to obtain the default score of the data table, and denoting the default scores of the data tables as y1, y2, y3, and y4;

[0049] Build a prediction model based on the default score:

[0050]

[0051] P is the credit score, that is, the prediction result of the prediction model, B is the basic score, β is the ratio of the number of historical loan customers with poor credit identification to the number of historical loan customers with good credit identification, and a, b, c, and d are the scale factors of each data table.

[0052] Preferably, the prediction module is specifically used for:

[0053] When the credit score is less than a preset first credit threshold, the credit identification of the current loan customer is determined to be poor, and an early warning prompt is issued;

[0054] When the credit score is between the first credit threshold and a preset second credit threshold, the credit indicator of the current loan customer is determined to be average, the loan amount and loan term of the current loan customer are reduced, and a safety reminder is issued;

[0055] When the credit score is greater than the second credit threshold, the credit identification of the current loan customer is determined to be good, and a safety prompt is issued. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flowchart of a financial loan risk prediction method based on big data according to an embodiment of the present invention;

[0057] Figure 2This is a structural block diagram of a financial loan risk prediction system based on big data according to an embodiment of the present invention.

[0058] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0060] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0061] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0062] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0063] Example 1

[0064] See also Figure 1 , is a flowchart of a financial loan risk prediction method based on big data in a first embodiment of the present invention, the method comprising the following steps:

[0065] Step S101: Acquire historical loan data of the loan platform, wherein the historical loan data includes the credit identification of each historical loan customer and a corresponding plurality of data tables, each data table including a plurality of data items;

[0066] Furthermore, the steps for obtaining the historical loan data of the loan platform specifically include:

[0067] Obtain historical loan data from the loan platform and pre-process the historical loan data; in this embodiment, pre-processing includes data cleaning and data completion;

[0068] The data table of each historical loan customer includes a basic information attribute table, a bank flow record table, an authentication information statistics table, and a loan information record table. The jth data table of the nth historical loan customer is

[0069]

[0070] The basic information attribute table includes data items such as gender, age, occupation, education level, marital status, and household registration type; the bank transaction record table includes data items such as annual transaction number, annual income, and annual expenditure; the authentication information statistics table includes data items such as credit verification, video authentication, mobile phone authentication, Taobao authentication, and household registration authentication; and the loan information record table includes data items such as annual loan number and annual loan amount.

[0071] Sort all the data items and record the i-th data item of the n-th historical loan customer as In this embodiment, the gender, age, occupation, education, marital status and household registration type of the basic information attribute table are respectively The annual transaction number, annual income and annual expenditure of the bank flow record are The authentication information statistics table includes credit authentication, video authentication, mobile phone authentication, Taobao authentication and household registration authentication. The annual number of loans and annual loan amounts in the loan information record table are

[0072] Among them, credit ratings include good, average and poor.

[0073] Step S102: Calculate the default coefficient of the data item based on the credit identification of the historical loan customer, and update the historical loan data according to the default coefficient; further steps specifically include:

[0074] Assign values to each data item of historical loan customers based on the preset assignment rules, and calculate the default coefficient of each data item:

[0075]

[0076] in, is the default coefficient of the i-th data item of the n-th historical loan customer, is the i-th data item among all historical loan customers and The number of equal values and credit identification as poor; is the i-th data item among all historical loan customers and The assignment of equal number;

[0077] The default coefficient With data items Make associations and traverse all historical loan customers to complete the update of historical customer loan data.

[0078] In this embodiment, the preset assignment rules are as follows: when the data item is gender, if the gender of the historical loan customer is male, the value is assigned to 1, and if the gender of the historical loan customer is female, the value is assigned to 2; when the data item is age, the age between 18 and 22 is assigned to 1, the age between 22 and 26 is assigned to 2, the age between 26 and 30 is assigned to 3, and so on, where the maximum loan age limit is 70 years old; similarly, when the data items are occupation, education level, marital status, and household registration type, values are assigned according to their types; When the data item is the annual number of transactions, take the annual maximum number of transactions and divide it into segments according to the numerical interval, and assign a value to each numerical interval. The annual number of transactions falls within the corresponding numerical interval, and its assignment is the assignment of the numerical interval. Similarly, the annual income, annual expenditure, annual number of loans and annual loan amount can be assigned. Credit certification can be assigned according to whether it is available. If credit certification exists, it is assigned a value of 1. If not, it is assigned a value of 0. Similarly, video authentication, mobile phone authentication, Taobao authentication and household authentication can be assigned values.

[0079] Step S103: Establish a prediction model based on the default coefficient, use the updated historical loan data as training samples, and train the prediction model. The steps specifically include:

[0080] Average the default coefficients corresponding to each data item in the data table to obtain the default score of the data table. The default scores of each data table are y1, y2, y3, and y4; where y1 is the basic information attribute table, y2 is the bank flow record table, y3 is the authentication information statistics table, and y4 is the loan information record table.

[0081] Build a predictive model based on default scores:

[0082]

[0083] P is the credit score, that is, the prediction result of the prediction model, B is the basic score, β is the ratio of the number of historical loan customers with poor credit identification to the number of historical loan customers with good credit identification, and a, b, c, and d are the scale factors of each data table.

[0084] Step S104: Obtain the loan data of the current loan customer, input the loan data of the current loan customer into the trained prediction model, obtain a prediction result, and issue a risk warning based on the prediction result; further, the step of issuing a risk warning based on the prediction result specifically includes:

[0085] When the credit score is lower than the preset first credit threshold, the credit mark of the current loan customer is determined to be poor and an early warning prompt is issued;

[0086] When the credit score is between the first credit threshold and the preset second credit threshold, the credit indicator of the current loan customer is determined to be average, and the loan amount and loan term of the current loan customer are reduced, and a safety reminder is issued;

[0087] When the credit score is greater than the second credit threshold, the credit identification of the current loan customer is determined to be good, and a safety reminder is issued.

[0088] Through the above steps, the historical loan data is divided into multiple data tables, and the default coefficient of each data item in the data table is calculated. The historical loan data is updated, and then a prediction model is established. The model is trained using the updated data. Based on big data analysis and prediction, a score that can accurately assess the credit of the current loan customer is obtained. The score is used to further determine whether to issue a loan to the customer, and the form of loan issuance is adjusted in a timely manner, thereby avoiding the customer's default risk and reducing the non-performing loan rate of the loan platform.

[0089] Example 2

[0090] See also Figure 2 , is a structural block diagram of a financial loan risk prediction system based on big data in a second embodiment of the present invention, the system comprising:

[0091] An acquisition module is used to acquire historical loan data of the loan platform, wherein the historical loan data includes the credit identification of each historical loan customer and a corresponding plurality of data tables, each data table including a plurality of data items;

[0092] A calculation module is used to calculate the default coefficient of a data item based on the credit identification of historical loan customers and update the historical loan data according to the default coefficient;

[0093] The training module is used to establish a prediction model based on the default coefficient and use the updated historical loan data as training samples to train the prediction model;

[0094] The prediction module is used to obtain the loan data of the current loan customer, input the loan data of the current loan customer into the trained prediction model, obtain the prediction results, and issue risk warnings based on the prediction results.

[0095] Furthermore, the acquisition module is specifically used to:

[0096] Obtain historical loan data from the loan platform and pre-process the historical loan data;

[0097] The data table of each historical loan customer includes a basic information attribute table, a bank flow record table, an authentication information statistics table, and a loan information record table. The jth data table of the nth historical loan customer is

[0098]

[0099] The basic information attribute table includes data items such as gender, age, occupation, education level, marital status, and household registration type; the bank transaction record table includes data items such as annual transaction number, annual income, and annual expenditure; the authentication information statistics table includes data items such as credit verification, video authentication, mobile phone authentication, Taobao authentication, and household registration authentication; and the loan information record table includes data items such as annual loan number and annual loan amount.

[0100] Sort all the data items and record the i-th data item of the n-th historical loan customer as

[0101] Among them, credit ratings include good, average and poor.

[0102] Furthermore, the calculation module is specifically used to:

[0103] Assign values to each data item of historical loan customers based on the preset assignment rules, and calculate the default coefficient of each data item:

[0104]

[0105] in, is the default coefficient of the i-th data item of the n-th historical loan customer, is the i-th data item among all historical loan customers and The number of equal values and credit identification as poor; is the i-th data item among all historical loan customers and The assignment of equal number;

[0106] The default coefficient With data items Make associations and traverse all historical loan customers to complete the update of historical customer loan data.

[0107] Furthermore, the training module is specifically used to:

[0108] Average the default coefficients corresponding to each data item in the data table to obtain the default score of the data table. The default scores of each data table are y1, y2, y3, and y4;

[0109] Build a predictive model based on default scores:

[0110]

[0111] P is the credit score, that is, the prediction result of the prediction model, B is the basic score, β is the ratio of the number of historical loan customers with poor credit identification to the number of historical loan customers with good credit identification, and a, b, c, and d are the scale factors of each data table.

[0112] Furthermore, the prediction module is specifically used to:

[0113] When the credit score is lower than the preset first credit threshold, the credit mark of the current loan customer is determined to be poor and an early warning prompt is issued;

[0114] When the credit score is between the first credit threshold and the preset second credit threshold, the credit indicator of the current loan customer is determined to be average, and the loan amount and loan term of the current loan customer are reduced, and a safety reminder is issued;

[0115] When the credit score is greater than the second credit threshold, the credit identification of the current loan customer is determined to be good, and a safety reminder is issued.

[0116] In specific implementation, historical loan data is divided into multiple data tables, and the default coefficient of each data item in the data table is calculated. The historical loan data is updated, and then a prediction model is established. The model is trained with the updated data. Based on big data analysis and prediction, a score that can accurately assess the credit of the current loan customer is obtained. The score is used to further determine whether to issue a loan to the customer, and the form of loan issuance is adjusted in a timely manner, thereby avoiding the customer's default risk and reducing the non-performing loan rate of the loan platform.

[0117] Example 3

[0118] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the financial loan risk prediction method based on big data of the above embodiment.

[0119] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, "computer-readable medium" can be any device that stores, communicates, propagates, or transmits a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0120] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0121] Among them, the memory may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a specific embodiment, the memory is a non-volatile memory. In a specific embodiment, the memory includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0122] Example 4

[0123] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a terminal, which includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the financial loan risk prediction method based on big data of the above embodiment.

[0124] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0126] Under the premise that no conflict occurs, those skilled in the art may freely combine and superimpose the above-mentioned additional technical features.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A financial loan risk prediction method based on big data, characterized in that: The method comprises: Acquiring historical loan data of the loan platform, wherein the historical loan data includes a credit identification of each historical loan customer and a corresponding plurality of data tables, each of the data tables including a plurality of data items; Calculating the default coefficient of the data item based on the credit identifier of the historical loan customer, and updating the historical loan data according to the default coefficient; Establishing a prediction model based on the default coefficient, and using the updated historical loan data as training samples to train the prediction model; Obtain loan data of the current loan customer, input the loan data of the current loan customer into the trained prediction model, obtain a prediction result, and issue a risk warning based on the prediction result.

2. The financial loan risk prediction method based on big data according to claim 1 is characterized in that: The steps to obtain historical loan data from the loan platform include: Obtaining historical loan data from a loan platform and preprocessing the historical loan data; The data table of each historical loan customer includes a basic information attribute table, a bank flow record table, an authentication information statistics table and a loan information record table. The jth data table of the nth historical loan customer is recorded as j∈{1,2,3,4}; The data items of the basic information attribute table include gender, age, occupation, education level, marital status, and household registration type; the data items of the bank transaction record table include annual transaction times, annual income, and annual expenditure; the data items of the authentication information statistics table include credit authentication, video authentication, mobile phone authentication, Taobao authentication, and household registration authentication; and the data items of the loan information record table include annual loan times and annual loan amount; Sort all the data items and record the i-th data item of the n-th historical loan customer as i∈{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}; Among them, the credit identification includes good, average and poor.

3. The financial loan risk prediction method based on big data according to claim 2 is characterized in that: The steps of calculating the default coefficient of the data item based on the credit identification of the historical loan customer and updating the historical loan data according to the default coefficient specifically include: Assign values to each of the data items of the historical loan customers based on a preset assignment rule, and calculate the default coefficient of each data item: in, is the default coefficient of the i-th data item of the n-th historical loan customer, The i-th data item and the The assigned values are equal and the credit identification is a poor quantity; The i-th data item and the The assignment of equal number; The default coefficient With the data item Perform association and traverse all the historical loan customers, thereby completing the update of the historical customer loan data.

4. The financial loan risk prediction method based on big data according to claim 2, characterized in that: The steps of establishing a prediction model based on the default coefficient specifically include: averaging the default coefficients corresponding to the data items in the data table to obtain the default score of the data table, and denoting the default scores of the data tables as y1, y2, y3, and y4; Build a prediction model based on the default score: P is the credit score, that is, the prediction result of the prediction model, B is the basic score, β is the ratio of the number of historical loan customers with poor credit identification to the number of historical loan customers with good credit identification, and a, b, c, and d are the scale factors of each data table.

5. The financial loan risk prediction method based on big data according to claim 4 is characterized in that: The steps of issuing risk warnings based on the prediction results specifically include: When the credit score is less than a preset first credit threshold, the credit identification of the current loan customer is determined to be poor, and an early warning prompt is issued; When the credit score is between the first credit threshold and a preset second credit threshold, the credit indicator of the current loan customer is determined to be average, the loan amount and loan term of the current loan customer are reduced, and a safety reminder is issued; When the credit score is greater than the second credit threshold, the credit identification of the current loan customer is determined to be good, and a safety prompt is issued.

6. A financial loan risk prediction system based on big data, characterized in that: The system comprises: An acquisition module is used to acquire historical loan data of the loan platform, wherein the historical loan data includes the credit identification of each historical loan customer and a corresponding plurality of data tables, each of the data tables including a plurality of data items; a calculation module, configured to calculate a default coefficient of the data item based on the credit identifier of the historical loan customer, and update the historical loan data according to the default coefficient; A training module, configured to establish a prediction model based on the default coefficient, and train the prediction model using the updated historical loan data as training samples; The prediction module is used to obtain the loan data of the current loan customer, input the loan data of the current loan customer into the trained prediction model, obtain the prediction result, and issue a risk warning based on the prediction result.

7. The financial loan risk prediction system based on big data according to claim 6 is characterized in that: The acquisition module is used to acquire historical loan data of the loan platform and pre-process the historical loan data; The data table of each historical loan customer includes a basic information attribute table, a bank flow record table, an authentication information statistics table and a loan information record table. The jth data table of the nth historical loan customer is recorded as j∈{1,2,3,4}; The data items of the basic information attribute table include gender, age, occupation, education level, marital status, and household registration type; the data items of the bank transaction record table include annual transaction times, annual income, and annual expenditure; the data items of the authentication information statistics table include credit authentication, video authentication, mobile phone authentication, Taobao authentication, and household registration authentication; and the data items of the loan information record table include annual loan times and annual loan amount; Sort all the data items and record the i-th data item of the n-th historical loan customer as i∈{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}; Among them, the credit identification includes good, average and poor.

8. The financial loan risk prediction system based on big data according to claim 7 is characterized in that: The calculation module is used to assign a value to each of the data items of the historical loan customers based on a preset assignment rule, and calculate the default coefficient of each of the data items: in, is the default coefficient of the i-th data item of the n-th historical loan customer, The i-th data item and the The assigned values are equal and the credit identification is a poor quantity; The i-th data item and the The assignment of equal number; The default coefficient With the data item Perform association and traverse all the historical loan customers, thereby completing the update of the historical customer loan data.

9. The financial loan risk prediction system based on big data according to claim 7, characterized in that: The training module is configured to average the default coefficients corresponding to the data items in the data table to obtain a default score for the data table, where the default scores for the data tables are denoted as y1, y2, y3, and y4; Build a prediction model based on the default score: P is the credit score, that is, the prediction result of the prediction model, B is the basic score, β is the ratio of the number of historical loan customers with poor credit identification to the number of historical loan customers with good credit identification, and a, b, c, and d are the scale factors of each data table.

10. The financial loan risk prediction system based on big data according to claim 9 is characterized in that: The prediction module includes: A first determination unit is configured to determine that the credit identification of the current loan customer is poor and issue an early warning prompt when the credit score is less than a preset first credit threshold; a second determination unit configured to, when the credit score is between the first credit threshold and a preset second credit threshold, determine that the credit identification of the current loan customer is fair, reduce the loan amount and loan term of the current loan customer, and issue a safety alert; The third determination unit is configured to determine that the credit identification of the current loan customer is good and issue a safety prompt when the credit score is greater than the second credit threshold.

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