CN114187096A - Risk assessment method, device and equipment based on user portrait and storage medium - Google Patents
Risk assessment method, device and equipment based on user portrait and storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a risk assessment method based on a user portrait, which comprises the following steps: generating a basic risk portrait according to the pre-loan data of the user, and calculating an initial risk value of the user according to the basic risk portrait; calculating a repayment intention value of the user according to the credited repayment data of the user; calculating a risk adjustment coefficient of the user according to overdue data of related products after the user is credited; inquiring a related user of the user, acquiring historical repayment data of the related user, and calculating a potential risk coefficient of the user according to the historical repayment data; and carrying out numerical adjustment on the initial risk value by using the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user. In addition, the invention also relates to a block chain technology, and pre-credit data can be stored in the nodes of the block chain. The invention also provides a risk assessment device based on the user portrait, electronic equipment and a storage medium. The invention can improve the accuracy of risk assessment.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a risk assessment method and device based on a user portrait, electronic equipment and a computer readable storage medium.
Background
With the coming of the credit society, people more and more commonly use credit consumption (loan) to realize a future prospective purchase plan, so that more and more loan companies or companies providing loan services emerge in the market, but the loan scale of each company is increasingly huge because people have more and more requirements on credit consumption, and when the loan amount reaches a certain scale, if a borrower cannot pay back in time, the cash flow of the company providing the loan is broken, so that the company has a large risk of bankruptcy and closure.
Most of the current methods for evaluating the loan risk of the user are based on the branch-line evaluation of the data analysis of the user before loan, that is, before the user is provided with the loan, the related information of the user is analyzed to determine the loan risk of the user, but when the loan time of the user is longer, the pre-evaluated loan risk becomes more and more inaccurate along with the time due to the prepositive property of the method.
Disclosure of Invention
The invention provides a risk assessment method and device based on a user portrait and a computer readable storage medium, and mainly aims to solve the problem of low accuracy in risk assessment.
In order to achieve the above object, the present invention provides a risk assessment method based on a user profile, comprising:
acquiring pre-loan data of a user, generating a basic risk portrait of the user according to the pre-loan data, and calculating an initial risk value of the user according to the basic risk portrait;
acquiring the loan repayment data of the user, and calculating the repayment intention value of the user according to the loan repayment data;
acquiring overdue data of the credited related products of the user, and calculating a risk adjustment coefficient of the user according to the overdue data of the credited related products;
inquiring a related user of the user, acquiring historical repayment data of the related user, and calculating a potential risk coefficient of the user according to the historical repayment data;
and carrying out numerical adjustment on the initial risk value by using the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user.
Optionally, the generating a basic risk representation of the user according to the pre-loan data includes:
selecting one data from the pre-loan data one by one as target data;
performing semantic extraction on the target data to obtain data semantics;
converting the data semantics of each target data into semantic vectors;
and splicing all semantic vectors into the basic risk portrait of the user.
Optionally, the semantic extracting the target data to obtain data semantics includes:
performing convolution and pooling on the target data to obtain low-dimensional feature semantics of the target data;
mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and screening the high-dimensional characteristic semantics by using a preset activation function to obtain data semantics.
Optionally, the stitching all semantic vectors into the basic risk representation of the user includes:
counting the vector length of each semantic vector;
selecting the semantic vector with the maximum vector length as a target vector, and determining the vector length of the target vector as a target length;
and extending the length of each semantic vector to the target length, and splicing each extended semantic vector as a row vector to obtain a basic risk portrait of the user.
Optionally, the calculating a repayment intention value of the user according to the post-credit repayment data includes:
acquiring the repayment period number and the total loan amount of the user;
counting the time repayment times of the user according to the loan repayment data, and counting the number of the repayment items of the user according to the loan repayment data;
dividing the total loan amount by the repayment item amount to obtain a first intention degree;
dividing the on-time repayment times by the repayment period number to obtain a second intention degree;
and weighting and summing the first intention degree and the second intention degree by using a preset weighting coefficient to obtain a repayment intention value.
Optionally, the calculating a risk adjustment coefficient of the user according to the overdue data of the post-loan related products includes:
counting the total quantity and the overdue product quantity of the relevant products after the loan in the overdue data;
and dividing the number of overdue products by the total number of the products to obtain a risk adjustment coefficient of the user.
Optionally, the calculating the potential risk coefficient of the user according to the historical repayment data includes:
counting the total number of users of the associated users and counting the number of overdue users with overdue repayment in the historical repayment data;
and calculating the difference between the total number of the users and the number of the overdue users, dividing the difference by the total number of the users, and calculating the square root of a result obtained by the division to obtain the potential risk coefficient.
In order to solve the above problem, the present invention further provides a risk assessment apparatus based on a user profile, the apparatus comprising:
the portrait generation module is used for acquiring pre-loan data of a user, generating a basic risk portrait of the user according to the pre-loan data, and calculating an initial risk value of the user according to the basic risk portrait;
the intention value analysis module is used for acquiring the post-loan repayment data of the user and calculating the repayment intention value of the user according to the post-loan repayment data;
the adjustment coefficient calculation module is used for acquiring overdue data of the credited related products of the user and calculating the risk adjustment coefficient of the user according to the overdue data of the credited related products;
the potential coefficient analysis module is used for inquiring the associated user of the user, acquiring historical repayment data of the associated user, and calculating the potential risk coefficient of the user according to the historical repayment data;
and the risk evaluation module is used for carrying out numerical adjustment on the initial risk value by utilizing the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the user representation-based risk assessment method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the user portrait based risk assessment method.
According to the embodiment of the invention, the basic risk portrait can be generated by using the pre-loan data of the user, the initial risk value of the user is calculated according to the portrait, meanwhile, the post-loan risk indexes such as repayment intention value, risk adjustment coefficient and potential risk coefficient are calculated according to the multi-aspect data after the user is credited, the initial risk value is subjected to numerical adjustment according to the post-loan risk indexes obtained through calculation, and the accuracy of risk evaluation on the user is improved. Therefore, the risk assessment method and device based on the user portrait, the electronic equipment and the computer readable storage medium can solve the problem of low accuracy in risk assessment.
Drawings
FIG. 1 is a schematic flow chart illustrating a risk assessment method based on a user profile according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a process of generating a basic risk representation of a user according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a process of calculating a repayment intention value of a user according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a risk assessment apparatus based on a user profile according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an electronic device implementing the user portrait-based risk assessment method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a risk assessment method based on a user portrait. The execution subject of the user portrait-based risk assessment method includes, but is not limited to, at least one of the electronic devices, such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the user-portrait-based risk assessment method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a risk assessment method based on a user profile according to an embodiment of the present invention is shown. In this embodiment, the user profile-based risk assessment method includes:
s1, acquiring pre-loan data of a user, generating a basic risk portrait of the user according to the pre-loan data, and calculating an initial risk value of the user according to the basic risk portrait.
In an embodiment of the present invention, the pre-loan data is data of the age, occupation, income condition, historical loan times, historical loan amount, historical overdue times, and the like of the user before the loan company provides a loan to the user.
In detail, a computer sentence with data fetching function (such as java sentence, python sentence, etc.) can be used to fetch the pre-credit data that the user authorization can be fetched from a predetermined storage area, wherein the storage area includes but is not limited to a database, a block chain node, and a network cache.
Specifically, the pre-loan data comprises multi-aspect data of the user, and represents various behavior performances of the user before loan, so that a basic risk representation of the user can be generated by using the pre-loan data, and an initial risk value of the user is calculated according to the basic risk representation, so that the risk of the user can be evaluated later.
In an embodiment of the present invention, referring to fig. 2, the generating a basic risk representation of the user according to the pre-loan data includes:
s21, selecting one data from the pre-loan data one by one as target data;
s22, performing semantic extraction on the target data to obtain data semantics;
s23, converting the data semantics of each target data into semantic vectors;
and S24, splicing all semantic vectors into the basic risk portrait of the user.
In an embodiment of the present invention, the target data may be selected from the pre-loan data sequentially, or may be selected from the pre-loan data randomly and unreplaced.
In the embodiment of the invention, a pre-constructed semantic analysis model is used for extracting the semantics of the target data to obtain the data semantics.
In detail, the semantic analysis Model includes, but is not limited to, a Natural Language Processing (NLP) Model, a Hidden Markov Model (HMM) Model.
For example, the target data is convolved, pooled and the like by using a pre-constructed semantic analysis model to extract the low-dimensional feature expression of the target data, the extracted low-dimensional feature expression is mapped to a pre-constructed high-dimensional space to obtain the high-dimensional feature expression of the low-dimensional feature, and the high-dimensional feature expression is selectively output by using a preset activation function to obtain data semantics.
In the embodiment of the present invention, the extracting semantics of the target data to obtain data semantics includes:
performing convolution and pooling on the target data to obtain low-dimensional feature semantics of the target data;
mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and screening the high-dimensional characteristic semantics by using a preset activation function to obtain data semantics.
In detail, the target data can be subjected to convolution and pooling processing through a semantic analysis model so as to reduce the data dimension of the target data, further reduce the occupation of calculation resources when the target data is analyzed, and improve the efficiency of semantic extraction.
Specifically, the low-dimensional feature semantics can be mapped to the pre-constructed high-dimensional space by using a preset mapping Function, wherein the mapping Function comprises a Gaussian Radial Basis Function, a Gaussian Function and the like in the MATLAB library.
For example, if the low-dimensional feature semantics are points in a two-dimensional plane, a mapping function may be used to calculate two-dimensional coordinates of the points in the two-dimensional plane to convert the two-dimensional coordinates into three-dimensional coordinates, and the calculated three-dimensional coordinates are used to map the points to a pre-constructed three-dimensional space, so as to obtain high-dimensional feature semantics of the low-dimensional feature semantics.
And mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space, so that the classifiability of the low-dimensional feature can be improved, and the accuracy of screening the features from the obtained high-dimensional feature semantics to obtain the data semantics is further improved.
In the embodiment of the invention, a preset activation function can be used for calculating an output value of each feature semantic in the high-dimensional feature semantics, and the feature semantics of which the output value is greater than a preset output threshold are selected as data semantics, wherein the activation function includes but is not limited to a sigmoid activation function, a tanh activation function and a relu activation function.
For example, the high-dimensional feature semantics include a feature semantic a, a feature semantic B, and a feature semantic C, and the feature semantic a, the feature semantic B, and the feature semantic C are respectively calculated by using an activation function, so that an output value of the feature semantic a is 80, an output value of the feature semantic B is 30, and an output value of the feature semantic C is 70, and when an output threshold value is 50, the feature semantic a and the feature semantic C are output as the data semantics of the target product.
In the embodiment of the invention, vector conversion can be carried out on the data semantics through a preset vector conversion model to obtain a semantic vector, wherein the vector conversion model comprises but is not limited to a word2vec model and a Bert model.
In the embodiment of the invention, after the semantic vectors are obtained, all the semantic vectors can be subjected to vector splicing to generate a basic risk portrait.
In the embodiment of the present invention, the stitching all semantic vectors into the basic risk representation of the user includes:
counting the vector length of each semantic vector;
selecting the semantic vector with the maximum vector length as a target vector, and determining the vector length of the target vector as a target length;
and extending the length of each semantic vector to the target length, and splicing each extended semantic vector as a row vector to obtain a basic risk portrait of the user.
In detail, since the length of each semantic vector may not be the same, in order to perform vector concatenation on the semantic vectors, the vector lengths of the semantic vectors need to be unified.
In the embodiment of the invention, the length of each semantic vector is counted, and the vectors with shorter vector lengths are subjected to vector extension, so that the lengths of all the semantic vectors are the same.
For example, if a first semantic vector is [11, 36, 22] and a second semantic vector is [14, 25, 31, 27] exist in the semantic vectors, statistics shows that the first vector length of the first semantic vector is 3, the second vector length of the second semantic vector is 4, and the second vector length is greater than the first vector length, the first semantic vector may be vector-extended by using a preset parameter (e.g., 0) until the first vector length is equal to the second vector length, so as to obtain the extended first semantic vector [11, 36, 22, 0 ].
In the embodiment of the invention, the two vectors can be subjected to column dimension combination by adding corresponding column elements in the two vectors.
For example, if there is a first semantic vector of [11, 36, 22, 0] and a second semantic vector of [14, 25, 31, 27], then the first semantic vector and the elements of the corresponding column in the second semantic vector may be added to obtain a base risk image of [25, 61, 53, 27 ].
In another embodiment of the invention, a matrix can be generated by using two vectors in a mode of parallel display of corresponding column elements in the two vectors, so that column dimension combination between the vectors is realized.
For example, the first semantic vector is [11, 36, 22, 0]]The second semantic vector is [14, 25, 31, 27]]Then, the elements of the corresponding columns in the first semantic vector and the second semantic vector can be displayed in parallel to obtain a matrix
And using the matrix as a basic risk representation of the user.
Furthermore, the basic risk portrait is a matrix obtained by splicing a plurality of vectors, so that the numerical value of the basic risk portrait can be directly obtained to obtain the initial risk value of the user.
And S2, acquiring the post-loan repayment data of the user, and calculating the repayment intention value of the user according to the post-loan repayment data.
In one practical application scenario of the present invention, because the initial risk value is generated according to the pre-loan data of the user, the initial risk value can only represent the risk of the user before the user is loaned, but cannot accurately represent the risk of repayment of the money item after the user is loaned, so the post-loan repayment data of the user can be obtained, the repayment behavior of the user is analyzed according to the repayment data of the user, the assessment of the post-loan risk is realized, and the timeliness and the accuracy of the subsequent assessment of the user risk value are improved.
In the embodiment of the invention, the repayment data after loan is the repayment amount, repayment time and other data of the payment for each repayment period of the user after loan.
In detail, the step of acquiring the post-loan repayment data of the user is the same as the step of acquiring the pre-loan data of the user in S1, and is not described herein again.
Further, the post-loan repayment data may be analyzed to calculate a repayment intention value of the user, where the repayment intention value is used to identify an intention of the user to repay on time.
In an embodiment of the present invention, referring to fig. 3, the calculating a repayment intention value of the user according to the post-loan repayment data includes:
s31, obtaining the repayment period number and the total loan amount of the user;
s32, counting the time repayment times of the user according to the loan repayment data, and counting the number of the repayment items of the user according to the loan repayment data;
s33, dividing the total loan amount by the number of paid items to obtain a first intention;
s34, dividing the on-time repayment times by the repayment period number to obtain a second intention degree;
and S35, carrying out weighted summation on the first intention degree and the second intention degree by utilizing a preset weight coefficient to obtain a repayment intention value.
In detail, the repayment period refers to the period of the user loan repayment in stages, and the total loan amount refers to the total amount of the user loan.
In particular, the post-loan payment data may be statistically determined to determine the number of outstanding on-time payments made by the user within the number of paid-back sessions, and the total amount of money the user has cleared.
In an embodiment of the present invention, the calculating the repayment intention degree of the user according to the repayment period number, the total loan amount, the on-time repayment times, and the repayment item number includes:
calculating the repayment intention degree by using the following weight algorithm:
wherein Y is the repayment intention, A is the amount of the paid money, B is the total loan amount, C is the repayment times on time, D is the repayment period number, and alpha and beta are preset weight coefficients.
And S3, obtaining overdue data of the credited related products of the user, and calculating the risk adjustment coefficient of the user according to the overdue data of the credited related products.
In an embodiment of the present invention, the expected data of the related loan products refers to overdue status data of other loan products held by the user when the user obtains the loan but does not pay for the loan.
In detail, when a user holds a plurality of loan products, if other loan products are overdue, the user may have a large overdue behavior on the loan.
Specifically, the step of obtaining the overdue data of the post-loan related product of the user is the same as the step of obtaining the pre-loan data of the user in S1, which is not described herein again.
In an embodiment of the present invention, the calculating a risk adjustment coefficient of the user according to the overdue data of the post-loan related product includes:
counting the total quantity and the overdue product quantity of the relevant products after the loan in the overdue data;
and dividing the number of overdue products by the total number of the products to obtain a risk adjustment coefficient of the user.
In detail, the obtaining the risk adjustment coefficient of the user by dividing the number of overdue products by the total number of products includes:
dividing the number of overdue products by the total number of products by using the following formula to obtain a risk adjustment coefficient of the user:
wherein X is the risk adjustment factor, E is the number of overdue products, and F is the total number of products.
S4, inquiring the associated user of the user, acquiring historical repayment data of the associated user, and calculating the potential risk coefficient of the user according to the historical repayment data.
In one practical application scenario of the present invention, it may be considered that there is a certain potential relationship between the habits and the payment repayment abilities of each person in the group where the user is located, for example, when there are a large number of associated users having loan overdue records among a plurality of associated users having an association relationship with the user, the loan overdue possibility of the user may be higher.
Therefore, the associated users of the users can be queried, historical repayment data of the associated users can be obtained, and the historical repayment data can be further analyzed to obtain the potential risk coefficient of the users through the historical repayment data of the associated users, wherein the historical repayment data refers to data such as loan records and overdue repayment records of each associated user in the associated users having an associated relationship with the users.
In the embodiment of the present invention, a pre-constructed associated user table may be obtained, an INDEX of the associated user table is created by using a CREATE INDEX function in an SQL library, and an associated user of the user is further queried from the associated user table according to the INDEX, where the associated user table includes a plurality of users and an association relationship between each user.
In detail, the step of acquiring the historical repayment data of the associated user is the same as the step of acquiring the pre-loan data of the user in S1, and is not described herein again.
In an embodiment of the present invention, the calculating the potential risk coefficient of the user according to the historical repayment data includes:
counting the total number of users of the associated users and counting the number of overdue users with overdue repayment in the historical repayment data;
and calculating the difference between the total number of the users and the number of the overdue users, dividing the difference by the total number of the users, and calculating the square root of a result obtained by the division to obtain the potential risk coefficient.
In detail, the calculating a difference between the total number of users and the number of overdue users, dividing the difference by the total number of users, and calculating a square root of a result of the division to obtain the risk potential coefficient includes:
calculating the potential risk coefficient of the user according to the total number of the users and the number of the overdue users by using the following formula:
wherein Z is the risk potential coefficient, G is the total number of users, and H is the number of overdue users.
And S5, carrying out numerical adjustment on the initial risk value by utilizing the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user.
In the embodiment of the present invention, since the initial risk value may only represent a risk before the user is credited, but may not accurately represent a risk of repayment of a money item after the user is credited, in order to improve accuracy of analyzing the risk value of the user, the initial risk value may be numerically adjusted by using an index obtained by analyzing post-credit data, such as the repayment intention value, the risk adjustment coefficient, and the potential risk coefficient, so as to improve accuracy of risk assessment for the user.
In an embodiment of the present invention, the numerically adjusting the initial risk value by using the repayment intention value, the risk adjustment coefficient, and the potential risk coefficient to obtain the actual risk value of the user includes:
and carrying out numerical adjustment on the initial risk value by using a numerical adjustment algorithm to obtain an actual risk value of the user:
S＝(X+Y+Z)*Q
wherein S is the actual risk value, Q is the initial risk value, X is the risk adjustment coefficient, Y is the repayment intention degree, and Z is the potential risk coefficient.
According to the embodiment of the invention, the basic risk portrait can be generated by using the pre-loan data of the user, the initial risk value of the user is calculated according to the portrait, meanwhile, the post-loan risk indexes such as repayment intention value, risk adjustment coefficient and potential risk coefficient are calculated according to the multi-aspect data after the user is credited, the initial risk value is subjected to numerical adjustment according to the post-loan risk indexes obtained through calculation, and the accuracy of risk evaluation on the user is improved. Therefore, the risk assessment method based on the user portrait can solve the problem of low accuracy in risk assessment.
FIG. 4 is a functional block diagram of a risk assessment apparatus based on a user profile according to an embodiment of the present invention.
The risk assessment apparatus 100 based on user profile of the present invention can be installed in an electronic device. According to the implemented functions, the user profile-based risk assessment apparatus 100 may include a profile generation module 101, an intention value analysis module 102, an adjustment coefficient calculation module 103, a potential coefficient analysis module 104, and a risk assessment module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the image generation module 101 is configured to obtain pre-loan data of a user, generate a basic risk image of the user according to the pre-loan data, and calculate an initial risk value of the user according to the basic risk image;
the intention value analysis module 102 is configured to obtain the post-loan repayment data of the user, and calculate a repayment intention value of the user according to the post-loan repayment data;
the adjustment coefficient calculation module 103 is configured to obtain overdue data of the post-loan related products of the user, and calculate a risk adjustment coefficient of the user according to the overdue data of the post-loan related products;
the potential coefficient analysis module 104 is configured to query a relevant user of the user, obtain historical repayment data of the relevant user, and calculate a potential risk coefficient of the user according to the historical repayment data;
the risk assessment module 105 is configured to perform numerical adjustment on the initial risk value by using the repayment intention value, the risk adjustment coefficient, and the potential risk coefficient to obtain an actual risk value of the user.
In detail, in the embodiment of the present invention, when the modules in the risk assessment apparatus 100 based on a user profile are used, the same technical means as the risk assessment method based on a user profile described in fig. 1 to 3 is adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a risk assessment method based on a user profile according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a user profile based risk assessment program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory 11 (for example, executing a risk assessment program based on a user profile, etc.), and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a risk assessment program based on a user profile, but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The memory 11 in the electronic device 1 stores a user representation-based risk assessment program that is a combination of instructions that, when executed in the processor 10, may implement:
acquiring pre-loan data of a user, generating a basic risk portrait of the user according to the pre-loan data, and calculating an initial risk value of the user according to the basic risk portrait;
acquiring the loan repayment data of the user, and calculating the repayment intention value of the user according to the loan repayment data;
acquiring overdue data of the credited related products of the user, and calculating a risk adjustment coefficient of the user according to the overdue data of the credited related products;
inquiring a related user of the user, acquiring historical repayment data of the related user, and calculating a potential risk coefficient of the user according to the historical repayment data;
and carrying out numerical adjustment on the initial risk value by using the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring pre-loan data of a user, generating a basic risk portrait of the user according to the pre-loan data, and calculating an initial risk value of the user according to the basic risk portrait;
acquiring the loan repayment data of the user, and calculating the repayment intention value of the user according to the loan repayment data;
acquiring overdue data of the credited related products of the user, and calculating a risk adjustment coefficient of the user according to the overdue data of the credited related products;
inquiring a related user of the user, acquiring historical repayment data of the related user, and calculating a potential risk coefficient of the user according to the historical repayment data;
and carrying out numerical adjustment on the initial risk value by using the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A user profile-based risk assessment method, the method comprising:
acquiring pre-loan data of a user, generating a basic risk portrait of the user according to the pre-loan data, and calculating an initial risk value of the user according to the basic risk portrait;
acquiring the loan repayment data of the user, and calculating the repayment intention value of the user according to the loan repayment data;
acquiring overdue data of the credited related products of the user, and calculating a risk adjustment coefficient of the user according to the overdue data of the credited related products;
inquiring a related user of the user, acquiring historical repayment data of the related user, and calculating a potential risk coefficient of the user according to the historical repayment data;
and carrying out numerical adjustment on the initial risk value by using the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user.
2. The user representation-based risk assessment method of claim 1, wherein said generating a base risk representation of said user from said pre-loan data comprises:
selecting one data from the pre-loan data one by one as target data;
performing semantic extraction on the target data to obtain data semantics;
converting the data semantics of each target data into semantic vectors;
and splicing all semantic vectors into the basic risk portrait of the user.
3. The user representation-based risk assessment method of claim 2, wherein said semantic extracting said target data to obtain data semantics comprises:
performing convolution and pooling on the target data to obtain low-dimensional feature semantics of the target data;
mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and screening the high-dimensional characteristic semantics by using a preset activation function to obtain data semantics.
4. The user representation-based risk assessment method of claim 2, wherein said stitching all semantic vectors into a base risk representation of the user comprises:
counting the vector length of each semantic vector;
selecting the semantic vector with the maximum vector length as a target vector, and determining the vector length of the target vector as a target length;
and extending the length of each semantic vector to the target length, and splicing each extended semantic vector as a row vector to obtain a basic risk portrait of the user.
5. The user profile-based risk assessment method of claim 1, wherein said calculating a repayment intent value of said user based on said post-loan repayment data comprises:
acquiring the repayment period number and the total loan amount of the user;
counting the time repayment times of the user according to the loan repayment data, and counting the number of the repayment items of the user according to the loan repayment data;
dividing the total loan amount by the repayment item amount to obtain a first intention degree;
dividing the on-time repayment times by the repayment period number to obtain a second intention degree;
and weighting and summing the first intention degree and the second intention degree by using a preset weighting coefficient to obtain a repayment intention value.
6. The user profile-based risk assessment method of claim 1, wherein said calculating a risk adjustment factor for said user based on said post-loan related product overdue data comprises:
counting the total quantity and the overdue product quantity of the relevant products after the loan in the overdue data;
and dividing the number of overdue products by the total number of the products to obtain a risk adjustment coefficient of the user.
7. The user representation-based risk assessment method according to any one of claims 1 to 6, wherein said calculating a potential risk coefficient of said user from said historical repayment data comprises:
counting the total number of users of the associated users and counting the number of overdue users with overdue repayment in the historical repayment data;
and calculating the difference between the total number of the users and the number of the overdue users, dividing the difference by the total number of the users, and calculating the square root of a result obtained by the division to obtain the potential risk coefficient.
8. A user profile based risk assessment apparatus, the apparatus comprising:
the portrait generation module is used for acquiring pre-loan data of a user, generating a basic risk portrait of the user according to the pre-loan data, and calculating an initial risk value of the user according to the basic risk portrait;
the intention value analysis module is used for acquiring the post-loan repayment data of the user and calculating the repayment intention value of the user according to the post-loan repayment data;
the adjustment coefficient calculation module is used for acquiring overdue data of the credited related products of the user and calculating the risk adjustment coefficient of the user according to the overdue data of the credited related products;
the potential coefficient analysis module is used for inquiring the associated user of the user, acquiring historical repayment data of the associated user, and calculating the potential risk coefficient of the user according to the historical repayment data;
and the risk evaluation module is used for carrying out numerical adjustment on the initial risk value by utilizing the repayment intention value, the risk adjustment coefficient and the potential risk coefficient to obtain an actual risk value of the user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a user representation-based risk assessment method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a user representation-based risk assessment method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115099680A (en) * | 2022-07-14 | 2022-09-23 | 平安科技（深圳）有限公司 | Risk management method, device, equipment and storage medium |
| CN115099680B (en) * | 2022-07-14 | 2024-02-02 | 平安科技（深圳）有限公司 | Risk management method, apparatus, device and storage medium |
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