WO2020077888A1 - Procédé et appareil permettant de déterminer la cote de solvabilité d'un emprunteur, et dispositif informatique - Google Patents

Procédé et appareil permettant de déterminer la cote de solvabilité d'un emprunteur, et dispositif informatique Download PDF

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WO2020077888A1
WO2020077888A1 PCT/CN2019/070361 CN2019070361W WO2020077888A1 WO 2020077888 A1 WO2020077888 A1 WO 2020077888A1 CN 2019070361 W CN2019070361 W CN 2019070361W WO 2020077888 A1 WO2020077888 A1 WO 2020077888A1
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data
loan
feature vector
credit
loan user
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PCT/CN2019/070361
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English (en)
Chinese (zh)
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龙撷宇
张敏
徐志成
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深圳壹账通智能科技有限公司
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Publication of WO2020077888A1 publication Critical patent/WO2020077888A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof

Definitions

  • This application relates to the field of computer technology. Specifically, this application relates to a method, apparatus, and computer equipment for calculating credit scores of loan users.
  • the score card technology is a large-scale automated processing method that is centered on computer technology and characterized by replacing manpower. It is a revolutionary measure currently adopted by commercial banks that can effectively control risks, reduce the number of business personnel, and greatly improve the efficiency of approval.
  • scorecards including marketing scoring, application scoring, behavioral scoring, and collection and recovery scoring, etc.
  • Loan company scoring cards are generally used for customer identification and loan loan amount assessment.
  • the inventor realizes that the current loan company basically uses the user to fill in the information when scoring with the scorecard technology, and pulls the credit information of the PBC for evaluation, and the result is obtained after one evaluation, but the probability of error in this way is relatively high.
  • this application proposes a method, device and computer equipment for calculating the credit score of a loan user, so as to reduce the probability of error in calculating the credit score of the loan user.
  • the embodiments of the present application provide a method for calculating the credit score of a loan user, including:
  • the feature vector of the loan user is input into a pre-built scoring model to obtain the credit score result of the loan user.
  • the embodiments of the present application also provide a credit score calculation device for loan users, including:
  • a data acquisition module for acquiring loan information filled in by the loan user, credit information of the loan user, transaction data of various payment cards held by the loan user, living payment data and policy data;
  • a feature vector generating module configured to generate a feature vector of the loan user based on the loan information, the credit data, the transaction data, the living payment data and the policy data;
  • the evaluation module is used to input the feature vector of the loan user into a pre-built scoring model to obtain the credit score result of the loan user.
  • an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the calculation of any of the above-mentioned loan user credit scores method.
  • an embodiment of the present application further provides a computer device, the computer device includes:
  • One or more processors are One or more processors;
  • Storage device for storing one or more programs
  • the one or more processors implement any one of the calculation methods for credit scores of loan users described above.
  • the above method, device and computer equipment for calculating the credit score of a loan user obtains the loan information and credit data of the loan user filled in by the loan user, as well as the transaction data, life payment data and policy data of each payment card held by the loan user
  • the user ’s pre-loan scoring card score can be updated in real time, reducing the probability of scoring errors once.
  • user transactions can be tracked in real time to prevent malicious transactions caused by users' malicious transactions before the end of the approval process, and they can also discover potential customers based on real-time score card scores.
  • FIG. 1 is a schematic flowchart of a method for calculating a credit score of a loan user according to an embodiment of the application
  • FIG. 2 is a schematic structural diagram of a loan user credit score calculation device according to an embodiment of the application.
  • FIG. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
  • a method for calculating the credit score of a loan user includes:
  • the loan information filled out by the loan user and the credit information of the loan user can be used as fixed input parameters, and the transaction data, life consumption data, and policy data of each payment card can be used as input parameters for real-time changes.
  • the information filled in by the loan user refers to the information filled in when the user submits the loan application.
  • the loan user is a user who has not yet submitted a loan application, the information filled in by the loan user can be replaced with 0 or other specified characters.
  • payment cards include physical cards such as bank cards and credit cards, and may also include virtual cards such as WeChat and Alipay.
  • Living consumption data refers to living bills, such as the location and amount of water, electricity and coal payment in the living bill.
  • Policy data refers to the sum insured, premiums, insured period, insured items, time of insured, etc. in the policy.
  • the insurance policy is the insurance policy, which refers to the written certificate of the insurance contract signed between the insurer and the insured.
  • the loan information filled out by the loan user and the credit information of the loan user can be obtained from the PBOC credit information system.
  • the transaction data of the physical card in each payment card held by the loan user can be obtained from the business system of each bank, and the transaction data of the virtual card in each payment card held by the loan user can be obtained from the commonly used payment APP (Application, application) Obtained from the back-end system, payment apps include WeChat, Alipay, Apple Pay (Apple Pay) and so on.
  • Life payment data can be obtained from the business system of each bank and the commonly used payment APP back-end system that can make life payment.
  • the policy data can be obtained from various insurance systems, for example, from the Ping An Insurance system.
  • the feature vectors of loan users are used to characterize the unique attributes of loan users. There are many ways to generate the feature vector of the loan user. For example, in one embodiment, the generation based on the loan information, the credit data, the transaction data, the living payment data and the policy data
  • the feature vector of the loan user includes:
  • S121 Classify the loan information, the credit information data, the transaction data, the living payment data, and the policy data to obtain character type data and numeric type data.
  • Character type data refers to text data type data without computing power, which includes Chinese characters, English characters, numeric characters and other ASC II (American Standard Code for Information Interchange) characters.
  • Numerical data metric data is an observation value measured on a digital scale, and the result is expressed as a specific numerical value.
  • the loan information, the credit information data, the transaction data, the living payment data and the policy data are classified to obtain character type data and numeric type data.
  • the method includes: deleting duplicate data in the transaction data, the living payment data and the order data. After obtaining transaction data, life payment data and the order data, it is also necessary to preprocess the above data to remove duplicate data. For example, if the user pays for life through a bank card, the transaction data of the bank card includes this information, life The consumption data also includes this item of information, and only one item of information may be retained.
  • the generating the first feature vector of the character type data includes: performing one-hot encoding on the character type data to generate the first feature vector of the character type data.
  • One hot encoding is a process of converting categorical variables into a form that is easy to use by machine learning algorithms.
  • the specific encoding method can be implemented according to the existing methods in the prior art.
  • the generating the first feature vector of the character type data includes: performing hash calculation on the character type data to generate the first feature vector of the character type data.
  • Hash refers to transforming an input of any length into a fixed-length output through a hash algorithm, and the output is a hash value. By performing hash calculation on the character type data, the first feature vector of the character type data can be obtained.
  • the generating the second feature vector of the numeric type data includes: creating an initial feature vector; and sequentially placing the numeric type data in a preset order Fill in the initial feature vector to obtain the second feature vector of the numeric type data.
  • the initial feature vector is an empty vector, and the size can be determined according to the number of acquired numeric type data.
  • the preset order can be set according to the actual needs of the user, for example, in accordance with the order of loan information, credit data, transaction data, living payment data and policy data, the extracted corresponding numerical type data are sequentially filled into the initial feature vector, then The second feature vector of numeric data can be obtained.
  • S124 Combine the first feature vector and the second feature vector to generate a feature vector of the loan user.
  • the first feature vector and the second feature vector are combined to generate the loan user's feature vector, which is the input feature of the scoring model.
  • the merging method may be that the first feature vector is in front, the second feature vector is in the back, or the second feature vector is in front, and the first feature vector is in the back, or other merging methods may be used.
  • the training model is used to train the scoring model to obtain a trained scoring model.
  • the training sample includes loan information and credit data filled in by each sample user, as well as transaction data, life consumption data, and policy data of each payment card held by the sample user.
  • the scoring model is:
  • Y is a credit score result
  • a1, a2 ... an are coefficients
  • X1, X2 ... Xn are various parameters in the feature vector of the loan user.
  • the scoring model may also be a logistic regression model, a neural network model, a decision tree model, and so on.
  • the obtaining a credit score result of the loan user further includes: according to the credit score result, issuing a payment corresponding to the credit score result to the loan user.
  • the loan company can determine whether to issue a loan to the user and the amount of the loan based on the credit score result.
  • the loan company intelligently recommends the corresponding amount of loan products based on the credit score result, so as to tap potential customers.
  • the loan information and credit information of the user can be replaced by 0 or other specified characters.
  • the transaction data of the physical card in each payment card held by the user can be obtained from the business system of each bank, and the transaction data of the virtual card in each payment card held by the user can be obtained from the commonly used payment APP background system.
  • Life payment data can be obtained from the business system of each bank and the commonly used payment APP back-end system that can make life payment.
  • Policy data can be obtained from various insurance systems.
  • S2 Generate a user's feature vector based on loan information, credit information data, the transaction data, the living payment data, and the policy data.
  • step S2 is the same as step S120, except that the loan information and credit data in S2 are all replaced with 0 or other designated characters. If the loan information and credit data in this step are all replaced with 0 or other numbers, the loan information and credit data are classified into numeric type data. If the loan information and credit information in this step are all replaced with a or other characters, the loan information and credit information are classified into character type data.
  • the scoring model in step S3 adopts the same scoring model as in step S130.
  • the user's feature vector is input into a pre-built scoring model, and the user's feature vector is calculated by the scoring model to output the user's credit score result.
  • the loan amount corresponding to each loan product is generally different. Take the loan products of Ping An Pratt & Whitney as an example.
  • the small loan products include i-loan, the maximum loan amount is 30,000, and the large loan products include the home loan.
  • Zhai e business loan line is 150,000 to 5 million.
  • the correspondence between the range of credit score results and loan products can be set in advance. The lower the general credit score result, the lower the loan amount corresponding to the loan product. After obtaining the user's credit score result, determine the range of the credit score result, and then find the loan product to be recommended.
  • loan product can be directly pushed to the user via SMS or other instant messaging software. If there are multiple loan products to be recommended, you can push multiple loan products directly to users through SMS or other instant messaging software, or you can further screen multiple loan products and choose one from multiple loan products.
  • User loan products There are many ways to choose a loan product from multiple loan products. For example, two loan products have the same loan amount, but one loan product needs a mortgage to apply, and the other loan product can apply without a mortgage to obtain user data. It is found that the user does not have a mortgage and there is no real estate under his name, so he can only recommend loan products that can be applied without a mortgage to the user to improve the effectiveness of the recommendation.
  • this application also provides a credit score calculation device for loan users.
  • the specific implementation of the device of this application will be described in detail below with reference to the drawings.
  • a credit score calculation device for loan users includes:
  • the data obtaining module 210 is used to obtain loan information filled in by the loan user, credit information of the loan user, transaction data of each payment card held by the loan user, living payment data and policy data;
  • the feature vector generating module 220 is configured to generate a feature vector of the loan user based on the loan information, the credit information data, the transaction data, the living payment data and the policy data;
  • the evaluation module 230 is used to input the feature vector of the loan user into a pre-built scoring model to obtain the credit score result of the loan user.
  • it further includes a payment issuance module connected to the evaluation module 230, and the payment issuance module is used to issue payment corresponding to the credit scoring result to the loan user according to the credit scoring result.
  • the feature vector generation module 220 includes:
  • a classification unit for classifying the loan information, the credit information data, the transaction data, the living payment data and the policy data to obtain character type data and numeric type data;
  • a first feature vector generating unit configured to generate a first feature vector of the character type data
  • a second feature vector generating unit configured to generate a second feature vector of the numeric type data
  • the loan user feature vector generating unit is configured to combine the first feature vector and the second feature vector to generate the loan user feature vector.
  • the classification unit classifies the loan information, the credit data, the transaction data, the living payment data, and the policy data to obtain character type data and numeric type data. , Is also used to delete duplicate data in the transaction data, the living payment data and the order data.
  • the first feature vector generating unit performs one-hot encoding on the character type data to generate a first feature vector of the character type data.
  • the first feature vector generating unit performs hash calculation on the character type data to generate a first feature vector of the character type data.
  • the second feature vector generating unit creates an initial feature vector; the numeric type data is sequentially filled into the initial feature vector in a preset order to obtain a second feature vector of the numeric type data .
  • the scoring model is:
  • Y is a credit score result
  • a1, a2 ... an are coefficients
  • X1, X2 ... Xn are various parameters in the feature vector of the loan user.
  • An embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, any method for calculating a credit score of a loan user described above is implemented.
  • the storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory, read-only memory), RAM (Random Access Memory, immediately Memory), EPROM (Erasable Programmable Read-Only Memory, erasable programmable read-only memory), EEPROM (Electrically Erasable Programmable Read-Only Memory, electrically erasable programmable read-only memory), flash memory, magnetic cards or light cards. That is, a storage medium includes any medium that stores or transmits information in a readable form by a device (eg, a computer). It can be read-only memory, magnetic disk or optical disk.
  • An embodiment of the present application also provides a computer device.
  • the computer device includes:
  • One or more processors are One or more processors;
  • Storage device for storing one or more programs
  • the one or more processors implement any one of the calculation methods for credit scores of loan users described above.
  • FIG. 3 is a schematic structural diagram of a computer device of the present application, including a processor 320, a storage device 330, an input unit 340, a display unit 350, and other devices.
  • the storage device 330 may be used to store the application program 310 and each functional module, and the processor 320 runs the application program 310 stored in the storage device 330 to execute various functional applications and data processing of the device.
  • the storage device 330 may be an internal memory or an external memory, or include both internal and external memory.
  • the internal memory may include read-only memory, programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory.
  • the external storage can include hard disks, floppy disks, ZIP disks, U disks, magnetic tapes, etc.
  • the storage devices disclosed in this application include but are not limited to these types of storage devices.
  • the storage device 330 disclosed in this application is only an example and not a limitation.
  • the input unit 340 is used to receive input of a signal, and receive loan information filled in by a loan user, credit information of the loan user, transaction data of each payment card held by the loan user, life payment data, and policy data.
  • the input unit 340 may include a touch panel and other input devices.
  • the touch panel can collect the user's touch operations on or near it (such as the user's operation on the touch panel or near the touch panel using any suitable objects or accessories such as fingers, stylus, etc.), and according to the preset
  • the program drives the corresponding connection device; other input devices may include but are not limited to one or more of a physical keyboard, function keys (such as playback control keys, switch keys, etc.), trackball, mouse, joystick, etc.
  • the display unit 350 may be used to display information input by the user or information provided to the user and various menus of the computer device.
  • the display unit 350 may take the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the processor 320 is the control center of the computer equipment, connects various parts of the entire computer with various interfaces and lines, runs or executes the software programs and / or modules stored in the storage device 330, and calls the data stored in the storage device , Perform various functions and process data.
  • the computer device includes one or more processors 320, and one or more storage devices 330, and one or more application programs 310, wherein the one or more application programs 310 are stored in the storage device 330 It is configured to be executed by the one or more processors 320, and the one or more application programs 310 are configured to execute the method for calculating the credit score of the loan user described in the above embodiment.
  • the above credit user credit score calculation method, device and computer equipment, combined with various data and scoring models, can update the user ’s pre-loan score card score in real time, reduce the probability of an error in the score evaluation, and can track user transactions in real time to prevent users from ending the approval process Malicious transactions before lending cause bad debts, and you can also discover potential customers based on real-time score card scores.
  • steps in the flowchart of the drawings are displayed in order according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, there is no strict order limitation for the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and the order of execution is also It is not necessarily carried out sequentially, but may be executed in turn or alternately with at least a part of other steps or sub-steps or stages of other steps.

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Abstract

L'invention concerne un procédé et un appareil permettant de calculer la cote de solvabilité d'un emprunteur, et un dispositif informatique. Le procédé consiste à obtenir des informations de prêt remplies par l'emprunteur, des données de crédit de l'emprunteur, des données de transaction de chaque carte de paiement détenue par l'emprunteur, des données de services publics et de paiement de services, et des données de politique d'assurance (S110); générer un vecteur de caractéristiques de l'emprunteur en fonction des informations de prêt, des données de crédit, des données de transaction, des données de services publics et de paiement de services, et des données de police d'assurance (S120); et entrer le vecteur de caractéristiques de l'emprunteur dans un modèle de notation pré-construit, de façon à obtenir une cote de solvabilité de l'emprunteur (S130). Le procédé réduit la probabilité d'erreurs de calcul de cotes de solvabilité d'un emprunteur.
PCT/CN2019/070361 2018-10-16 2019-01-04 Procédé et appareil permettant de déterminer la cote de solvabilité d'un emprunteur, et dispositif informatique WO2020077888A1 (fr)

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CN116777608A (zh) * 2023-08-25 2023-09-19 北京佳格天地科技有限公司 基于大数据的农业金融风险监管系统、方法及存储介质
CN117670149A (zh) * 2024-02-01 2024-03-08 杭银消费金融股份有限公司 一种客群质量评分方法

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