Method and device for recommending credit institutions

Abstract

The invention relates to a credit institution recommendation method and device, and the method comprises the steps: obtaining a model score of a target user on each credit institution, and enabling the model scores to be used for indicating the probability that the credit institution carries out the credit granting of the target user; obtaining credit granting correlation among the plurality of credit institutions, the credit granting correlation being related to a first matrix and a second matrix, elements in the first matrix being model scores of different users in one of the plurality of credit institutions, and elements in the second matrix being model scores of different users in another credit institution; classifying the plurality of credit institutions according to the credit granting relevance; and selecting a target number of credit institutions from each category to form a target credit institution combination, and recommending the target credit institution combination to the target user. According to the method and the device, the problem that a loan assisting platform in the related technology is lack of a mature institution recommendation system is solved, and the effects of improving the accuracy of recommending the credit institution to the user and further improving the viscidity and dependency of the user on the credit platform are achieved.

Description

[0001] This application is a divisional application of the application filed on March 28, 2023, with application number 202310311172.X, entitled "A Recommendation Method and Apparatus for a Credit Institution". Technical Field

[0002] This invention relates to the field of financial credit technology, and in particular to a recommendation method, apparatus, computer equipment, and computer-readable storage medium for credit institutions. Background Technology

[0003] In the financial lending sector, especially in the online micro-loan field, loan applicants often connect with financial institutions through applications, software, or loan assistance platforms. These institutions or platforms then review the applicant's qualifications to determine whether to grant credit, as well as the credit limit and interest rates. In practice, many users apply for loans by downloading loan assistance platform apps, which connect users with multiple micro-loan institutions or funding sources to help them obtain loans. Through this mechanism, loan applicants only need to download one app to potentially connect with multiple financial institutions.

[0004] Different financial institutions naturally have different preferences regarding users and risks. Therefore, the same user will have different loan amounts, terms, and interest rates at different institutions, giving users more choices. Throughout the process, the loan facilitation platform, as the traffic provider, will also conduct certain reviews and risk controls on the user's qualifications. Users who do not meet the requirements will not be pushed to the institutions; while for users who do meet the requirements, the loan facilitation platform is working hard to build a recommendation system so that users can obtain loans through the loan facilitation platform.

[0005] Currently, there is no mature recommendation system for institutional recommendations on loan facilitation platforms. In practice, loan facilitation platforms generally face the following bottlenecks or problems: 1. What kind of users can meet the organization's requirements? How can this be quantified? 2. How can the correlation between institutional preferences and inter-institutional preferences be quantified? 3. How do the creditworthiness of the same user differ across different institutions? 4. Based on the likelihood of credit approval and the institution's preferences, what strategy should be developed to improve the credit approval rate (credit disbursement rate)? Currently, no effective solution has been proposed to address the lack of a mature institutional recommendation system for loan facilitation platforms using related technologies. Summary of the Invention

[0006] The purpose of this application is to address the shortcomings of existing technologies by providing a method, apparatus, computer equipment, and computer-readable storage medium for recommending credit institutions, thereby at least resolving the problem of the lack of a mature institutional recommendation system in loan facilitation platforms in related technologies.

[0007] To achieve the above objectives, the technical solution adopted in this application is as follows: In a first aspect, embodiments of this application provide a recommendation method for credit institutions, comprising: Obtain the model score of the target user on each credit institution, wherein the model score is used to indicate the probability that the credit institution grants credit to the target user; Obtain the credit correlation among multiple credit institutions, wherein the credit correlation is related to a first matrix and a second matrix, the elements in the first matrix are the model scores of different users in one of the multiple credit institutions, and the elements in the second matrix are the model scores of different users in another of the multiple credit institutions. The multiple credit institutions are classified according to the aforementioned credit relevance; Select a target number of credit institutions from each category to form a target credit institution portfolio and recommend it to the target user.

[0008] In some embodiments, obtaining credit correlation among multiple credit institutions includes: The credit correlation rho(A,B) between credit institution A and credit institution B is calculated using the following formula: Where u = [score(A1), score(A2), score(A3),...,score(An)], v = [score(B1), score(B2), score(B3),...,score(Bn)], where score(An) represents the model score of the nth user on credit institution A, and score(Bn) represents the model score of the nth user on credit institution B.

[0009] In some embodiments, classifying the plurality of credit institutions according to the credit relevance includes: Credit institutions among the multiple credit granting institutions whose pairwise credit correlation exceeds a first value are classified into the first category; Credit institutions whose credit relevance to the remaining credit institutions among the plurality of credit institutions is lower than the second value are classified into the second category; For the remaining credit granting institutions other than the first and second categories among the multiple credit granting institutions, if the credit correlation between the remaining credit granting institutions and more than the target number of credit granting institutions in the first category exceeds the first value, then the remaining credit granting institutions are classified into the first category; otherwise, the remaining credit granting institutions are classified into the third category.

[0010] In some embodiments, selecting a target number of credit institutions from each category to form a target portfolio of credit institutions and recommending it to the target user includes: Based on the ranking of the credit granting probability of credit institutions within each category, the target number of credit institutions are selected from each category to form a target credit institution portfolio; The target credit institution portfolio is recommended to the target user.

[0011] In some embodiments, after selecting a target number of credit institutions from each category to form a target credit institution portfolio and recommending it to the target user, the method further includes: The overall credit probability p(A,B) of the target user on the target credit institution portfolio is calculated using the following formula, where the target credit institution portfolio includes credit institution A and credit institution B: Where p1 represents the probability of the target user being granted credit by credit institution A, and p2 represents the probability of the target user being granted credit by credit institution B.

[0012] Secondly, embodiments of this application provide a recommendation device for a credit institution, comprising: The first acquisition unit is used to acquire the model score of the target user on each credit institution, wherein the model score is used to indicate the probability that the credit institution grants credit to the target user; The second acquisition unit is used to acquire the credit correlation among multiple credit institutions, wherein the credit correlation is related to a first matrix and a second matrix, the elements in the first matrix are the model scores of different users in one of the multiple credit institutions, and the elements in the second matrix are the model scores of different users in another of the multiple credit institutions. A classification unit is used to classify the plurality of credit institutions according to the credit relevance; The recommendation unit is used to select a target number of credit institutions from each category to form a target credit institution portfolio and recommend it to the target user.

[0013] In some embodiments, the second acquisition unit includes: The calculation module is used to calculate the credit correlation rho(A,B) between credit institution A and credit institution B according to the following formula: Where u = [score(A1), score(A2), score(A3),...,score(An)], v = [score(B1), score(B2), score(B3),...,score(Bn)], where score(An) represents the model score of the nth user on credit institution A, and score(Bn) represents the model score of the nth user on credit institution B.

[0014] In some embodiments, the classification unit includes: The first classification module is used to classify credit institutions among the multiple credit granting institutions whose pairwise credit correlation exceeds a first value into the first category; The second classification module is used to classify credit institutions whose credit relevance with the other credit institutions among the plurality of credit institutions is lower than the second value into the second category; The third classification module is used to classify the remaining credit granting institutions (excluding the first and second categories) into the first category if the credit correlation between the remaining credit granting institutions and the credit granting institutions in the first category exceeding the target number exceeds the first value; otherwise, the remaining credit granting institutions are classified into the third category.

[0015] Thirdly, embodiments of this application provide a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the recommendation method of the credit institution as described in the first aspect above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the recommendation method of a credit institution as described in the first aspect above.

[0017] This application adopts the above technical solution. Compared with the prior art, the credit institution recommendation method provided in this application embodiment obtains the model score of the target user on each credit institution, and then obtains the credit correlation between multiple credit institutions; then classifies the multiple credit institutions according to the credit correlation; finally, selects a target number of credit institutions from each category to form a target credit institution combination and recommends it to the target user. This solves the problem of the lack of a mature institution recommendation system in loan assistance platforms in related technologies, and achieves the effect of improving the accuracy of recommending credit institutions to users, thereby increasing the stickiness and dependence of users on the credit platform.

[0018] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a structural block diagram of a mobile terminal according to an embodiment of this application; Figure 2 This is a flowchart of a recommendation method for a credit institution according to an embodiment of this application; Figure 3 This is a structural block diagram of a credit institution's recommendation device according to an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0021] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0022] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0023] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; 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 not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0024] This embodiment provides a mobile terminal. Figure 1 This is a structural block diagram of a mobile terminal according to an embodiment of this application. Figure 1As shown, the mobile terminal includes: a radio frequency (RF) circuit 110, a memory 120, an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a wireless fidelity (WiFi) module 170, a processor 180, and a power supply 190, among other components. Those skilled in the art will understand that... Figure 1 The mobile terminal structure shown does not constitute a limitation on the mobile terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0025] The following is combined Figure 1 A detailed introduction to each component of a mobile terminal: RF circuit 110 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink information from the base station and processes it with processor 180; additionally, it transmits uplink data to the base station. Typically, RF circuitry includes, but is not limited to, antennas, at least one amplifier, transceiver, coupler, low-noise amplifier (LNA), duplexer, etc. Furthermore, RF circuit 110 can also communicate wirelessly with networks and other devices. The aforementioned wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0026] The memory 120 can be used to store software programs and modules. The processor 180 executes various functional applications and data processing of the mobile terminal by running the software programs and modules stored in the memory 120. The memory 120 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile terminal (such as audio data, phone book, etc.). In addition, the memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0027] The input unit 130 can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the input unit 130 may include a touch panel 131 and other input devices 132. The touch panel 131, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 131), and drive the corresponding connection devices according to a pre-set program. Optionally, the touch panel 131 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends it to the processor 180, and can receive and execute commands sent by the processor 180. In addition, the touch panel 131 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 131, the input unit 130 may also include other input devices 132. Specifically, other input devices 132 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0028] Display unit 140 can be used to display information input by the user or information provided to the user, as well as various menus of the mobile terminal. Display unit 140 may include a display panel 141, optionally configured as a Liquid Crystal Display (LCD), Organic Light-Emitting Diode (OLED), or similar display panel 141. Further, touch panel 131 may cover display panel 141. When touch panel 131 detects a touch operation on or near it, it transmits the information to processor 180 to determine the type of touch event. Subsequently, processor 180 provides corresponding visual output on display panel 141 based on the type of touch event. Although in Figure 1 In this embodiment, the touch panel 131 and the display panel 141 are two separate components to realize the input and output functions of the mobile terminal. However, in some embodiments, the touch panel 131 and the display panel 141 can be integrated to realize the input and output functions of the mobile terminal.

[0029] The mobile terminal may also include at least one sensor 150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 141 according to the ambient light level, and the proximity sensor can turn off the display panel 141 and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used for applications that identify the mobile terminal's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition-related functions (such as pedometers, taps), etc. Other sensors that may be configured in the mobile terminal, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0030] The speaker 161 and microphone 162 in the audio circuit 160 provide an audio interface between the user and the mobile terminal. The audio circuit 160 can convert the received audio data into electrical signals and transmit them to the speaker 161, where the speaker 161 converts them into sound signals for output. On the other hand, the microphone 162 converts the collected sound signals into electrical signals, which are received by the audio circuit 160, converted into audio data, and then output to the processor 180 for processing. After processing, the audio data is transmitted via the RF circuit 110 to, for example, another mobile terminal, or the audio data is output to the memory 120 for further processing.

[0031] WiFi is a short-range wireless transmission technology. Mobile terminals using a WiFi module 170 can help users send and receive emails, browse web pages, and access streaming media, providing users with wireless broadband internet access. Although Figure 1 WiFi module 170 is shown, but it is understood that it is not a necessary component of a mobile terminal and can be omitted or replaced with other short-range wireless transmission modules, such as Zigbee modules or WAPI modules, as needed without changing the nature of the invention.

[0032] The processor 180 is the control center of the mobile terminal. It connects various parts of the mobile terminal via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 120, and by calling data stored in the memory 120, it performs various functions and processes data of the mobile terminal, thereby providing overall monitoring of the mobile terminal. Optionally, the processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 180.

[0033] The mobile terminal also includes a power supply 190 (such as a battery) that supplies power to various components. Preferably, the power supply can be logically connected to the processor 180 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0034] Although not shown, mobile terminals may also include cameras, Bluetooth modules, etc., which will not be described in detail here.

[0035] In this embodiment, the processor 180 is configured as follows: Obtain the model score of the target user on each credit institution, wherein the model score is used to indicate the probability that the credit institution grants credit to the target user; Obtain the credit correlation among multiple credit institutions, wherein the credit correlation is related to a first matrix and a second matrix, the elements in the first matrix are the model scores of different users in one of the multiple credit institutions, and the elements in the second matrix are the model scores of different users in another of the multiple credit institutions. The multiple credit institutions are classified according to the aforementioned credit relevance; Select a target number of credit institutions from each category to form a target credit institution portfolio and recommend it to the target user.

[0036] In some embodiments, the processor 180 is further configured to: The credit correlation rho(A,B) between credit institution A and credit institution B is calculated using the following formula: Where u = [score(A1), score(A2), score(A3),...,score(An)], v = [score(B1), score(B2), score(B3),...,score(Bn)], where score(An) represents the model score of the nth user on credit institution A, and score(Bn) represents the model score of the nth user on credit institution B.

[0037] In some embodiments, the processor 180 is further configured to: Credit institutions among the multiple credit granting institutions whose pairwise credit correlation exceeds a first value are classified into the first category; Credit institutions whose credit relevance to the remaining credit institutions among the plurality of credit institutions is lower than the second value are classified into the second category; For the remaining credit granting institutions other than the first and second categories among the multiple credit granting institutions, if the credit correlation between the remaining credit granting institutions and more than the target number of credit granting institutions in the first category exceeds the first value, then the remaining credit granting institutions are classified into the first category; otherwise, the remaining credit granting institutions are classified into the third category.

[0038] In some embodiments, the processor 180 is further configured to: Based on the ranking of the credit granting probability of credit institutions within each category, the target number of credit institutions are selected from each category to form a target credit institution portfolio; The target credit institution portfolio is recommended to the target user.

[0039] In some embodiments, the processor 180 is further configured to: After selecting a target number of credit institutions from each category to form a target credit institution portfolio and recommending it to the target user, the overall credit probability p(A,B) of the target user on the target credit institution portfolio is calculated according to the following formula, where the target credit institution portfolio includes credit institution A and credit institution B: Where p1 represents the probability of the target user being granted credit by credit institution A, and p2 represents the probability of the target user being granted credit by credit institution B.

[0040] This embodiment provides a recommendation method for credit institutions. Figure 2 This is a flowchart of a recommendation method for a credit institution according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the model score of the target user on each credit institution, wherein the model score is used to indicate the probability that the credit institution grants credit to the target user; Step S202: Obtain the credit correlation among multiple credit institutions, wherein the credit correlation is related to a first matrix and a second matrix, the elements in the first matrix are the model scores of different users in one of the multiple credit institutions, and the elements in the second matrix are the model scores of different users in another of the multiple credit institutions. Step S203: Classify the multiple credit institutions according to the credit relevance; Step S204: Select a target number of credit institutions from each category to form a target credit institution portfolio and recommend it to the target user.

[0041] Through the above steps, the model score of the target user on each credit institution is obtained, and then the credit correlation between multiple credit institutions is obtained; then, the multiple credit institutions are classified according to the credit correlation; finally, a target number of credit institutions are selected from each category to form a target credit institution portfolio and recommended to the target user. This solves the problem of the lack of a mature institution recommendation system in loan assistance platforms in related technologies, and improves the accuracy of recommending credit institutions to users, thereby increasing users' stickiness and dependence on the credit platform.

[0042] In some embodiments, this application builds a credit granting model for each credit granting institution. Through feature engineering, effective variables are extracted from the model, and machine learning methods are used to construct the credit granting model. Online verification shows that the overall false positive rate of the credit granting model can be controlled within 10%. Therefore, it can be considered that the model score can express the institution's preferences very well. This application does not specifically limit the details of the modeling process.

[0043] Based on the credit granting model, for any user (labeled as numbers 1, 2, 3...) and any credit granting institution (labeled as letters A, B, C...), their model score and corresponding credit granting probability, namely Score(A1) and P(A1), can be calculated. Therefore, for all online credit granting institutions A, B, C..., P(A1), P(B1),... can be calculated. Then, by sorting these probabilities, without loss of generality, we can consider... P(A1)>= P(B1)>= P(C1)>=... To create a relatively "basic" recommendation strategy, one could simply sort users by credit granting probabilities and push them to credit institutions with relatively high credit granting probabilities. However, this strategy doesn't consider the preferences of different institutions for users, making it unlikely to maximize credit granting rates. Based on this consideration, this application proposes a more efficient recommendation strategy. This strategy calculates the credit granting probability of each user across various credit institutions, categorizes institutions by constructing an inter-institutional credit correlation matrix, selects the most suitable institution within each category, and pushes it to the user. Finally, it calculates the user's overall credit granting probability across the recommended institution combination to evaluate the performance of the recommendation strategy.

[0044] There is a correlation between the lending behaviors of different credit institutions. This stems from the fact that each credit institution may have a preference for a certain type of user. Some credit institutions have similar and close preferences, while others have more unique and distinctive preferences. Therefore, clarifying this information helps in understanding the overall characteristics of each institution.

[0045] In some embodiments, obtaining credit correlation among multiple credit institutions includes: The credit correlation rho(A,B) between credit institution A and credit institution B is calculated using the following formula: Where u = [score(A1), score(A2), score(A3),...,score(An)], v = [score(B1), score(B2), score(B3),...,score(Bn)], where score(An) represents the model score of the nth user on credit institution A, and score(Bn) represents the model score of the nth user on credit institution B.

[0046] In some embodiments, classifying the plurality of credit institutions according to the credit relevance includes: Credit institutions among the multiple credit granting institutions whose pairwise credit correlation exceeds a first value are classified into the first category; Credit institutions whose credit relevance to the remaining credit institutions among the plurality of credit institutions is lower than the second value are classified into the second category; For the remaining credit granting institutions other than the first and second categories among the multiple credit granting institutions, if the credit correlation between the remaining credit granting institutions and more than the target number of credit granting institutions in the first category exceeds the first value, then the remaining credit granting institutions are classified into the first category; otherwise, the remaining credit granting institutions are classified into the third category.

[0047] For example, the classification process is as follows: Step 1: All institutions with pairwise correlations exceeding 60% are classified into Category 1; Step 2: Any institution whose correlation with all other institutions is less than 30% will be classified into the second category. Step 3: Among the remaining institutions, if their correlation with more than 2 / 3 of the institutions in Category 1 exceeds 60%, they will be classified into Category 1; otherwise, they will be classified into the remaining Category 3. In practice, the first category often covers the most institutions, typically accounting for half or even two-thirds of the total. This categorization ensures high correlation between credit granting within a single category and low correlation between categories. Generally, three to four categories are appropriate. Furthermore, Principal Component Analysis (PCA) was used to cross-validate the number of categories, concluding that three to four categories can cover at least 90% of the variability.

[0048] In some embodiments, selecting a target number of credit institutions from each category to form a target portfolio of credit institutions and recommending it to the target user includes: Based on the ranking of the credit granting probability of credit institutions within each category, the target number of credit institutions are selected from each category to form a target credit institution portfolio; The target credit institution portfolio is recommended to the target user.

[0049] In some embodiments, after selecting a target number of credit institutions from each category to form a target credit institution portfolio and recommending it to the target user, the method further includes: The overall credit probability p(A,B) of the target user on the target credit institution portfolio is calculated using the following formula, where the target credit institution portfolio includes credit institution A and credit institution B: Where p1 represents the probability of the target user being granted credit by credit institution A, and p2 represents the probability of the target user being granted credit by credit institution B.

[0050] As can be seen from the formula above, if the credit lines of two institutions are highly correlated (rho = 1), then p1 = p2, and it can be easily deduced that p(A,B) = p1 = p2. In other words, if two institutions are highly correlated, the overall credit probability will not increase, which directly affects our institution selection strategy. The formula above can also be iterated and extended. We can consider B to represent the overall credit probability of a user across institutions C and D. Using the formula above, we can actually calculate the overall credit probability of a user across institutions A, C, and D, i.e., the probability of obtaining credit from at least one institution. The correlation can be estimated using the model's mean. The same logic applies to other institutions.

[0051] In this embodiment of the application, when selecting credit granting institutions, efforts will be made to ensure that institutions are included in each major category, or in other words, the coverage of institutions will be as extensive as possible, spanning as many institutional categories as possible. Specifically, several institutions with the highest credit granting probability in each major category will be selected. This will allow for greater potential for growth in the overall credit granting rate. Then, the overall credit granting probability will be calculated based on the selected institutions to infer the overall advancement effect of the strategy.

[0052] Furthermore, to prevent excessive push notifications to organizations, this embodiment of the application requires that only a maximum of half of the organizations in each major category can be pushed. This way, when users log in again, they will still have a certain number of organizations to apply to, increasing user stickiness and reliance on the app.

[0053] As shown in Table 1 below, a large number of user application behaviors within a certain period were calculated. Operation strategy V1 is a static strategy based on operation batches, while operation strategy V2 is a dynamic recommendation strategy corresponding to this application.

[0054] Table 1 As can be seen, the recommended strategy in this application embodiment can reduce the number of institutions applying for credit to a certain extent. However, in terms of the overall credit line (credit rate), both operating systems show an increase, especially on the Android system with a larger data volume, the improvement is very obvious, reaching a relative 30%.

[0055] The embodiments of this application can achieve the following technical effects: First, it can accurately match users and organizations; Second, increase the probability of credit approval for users and the utilization rate of application quotas for institutions; Third, increase user stickiness to the app and the sustainability of loan applications.

[0056] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0057] This embodiment provides a recommendation device for a credit institution, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0058] Figure 3 This is a structural block diagram of a credit institution's recommendation device according to an embodiment of this application, such as... Figure 3 As shown, the device includes: The first acquisition unit 31 is used to acquire the model score of the target user on each credit institution, wherein the model score is used to indicate the probability that the credit institution grants credit to the target user; The second acquisition unit 32 is used to acquire the credit correlation among multiple credit institutions, wherein the credit correlation is related to a first matrix and a second matrix, the elements in the first matrix are the model scores of different users in one of the multiple credit institutions, and the elements in the second matrix are the model scores of different users in another of the multiple credit institutions. Classification unit 33 is used to classify the plurality of credit institutions according to the credit relevance; Recommendation unit 34 is used to select a target number of credit institutions from each category to form a target credit institution portfolio and recommend it to the target user.

[0059] In some embodiments, the second acquisition unit 32 includes: The calculation module is used to calculate the credit correlation rho(A,B) between credit institution A and credit institution B according to the following formula: Where u = [score(A1), score(A2), score(A3),...,score(An)], v = [score(B1), score(B2), score(B3),...,score(Bn)], where score(An) represents the model score of the nth user on credit institution A, and score(Bn) represents the model score of the nth user on credit institution B.

[0060] In some embodiments, the classification unit 33 includes: The first classification module is used to classify credit institutions among the multiple credit granting institutions whose pairwise credit correlation exceeds a first value into the first category; The second classification module is used to classify credit institutions whose credit relevance with the other credit institutions among the plurality of credit institutions is lower than the second value into the second category; The third classification module is used to classify the remaining credit granting institutions (excluding the first and second categories) into the first category if the credit correlation between the remaining credit granting institutions and the credit granting institutions in the first category exceeding the target number exceeds the first value; otherwise, the remaining credit granting institutions are classified into the third category.

[0061] In some embodiments, the recommendation unit 34 includes: The sorting module is used to select the target number of credit institutions from each category to form a target credit institution portfolio according to the sorting of the credit granting probability of credit institutions within each category; The recommendation module is used to recommend the target credit institutions to the target user.

[0062] In some embodiments, the device further includes: A calculation unit is configured to, after selecting a target number of credit institutions from each category to form a target credit institution portfolio and recommending it to the target user, calculate the overall credit probability p(A,B) of the target user on the target credit institution portfolio according to the following formula, wherein the target credit institution portfolio includes credit institution A and credit institution B: Where p1 represents the probability of the target user being granted credit by credit institution A, and p2 represents the probability of the target user being granted credit by credit institution B.

[0063] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0064] An embodiment provides a computer device. The recommendation method for credit institutions, in conjunction with this application embodiment, can be implemented using a computer device. Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of this application.

[0065] The computer device may include a processor 41 and a memory 42 storing computer program instructions.

[0066] Specifically, the processor 41 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0067] The memory 42 may include a large-capacity memory for data or instructions. For example, and not limitingly, the memory 42 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 42 may include removable or non-removable (or fixed) media. Where appropriate, the memory 42 may be internal or external to a data processing device. In a particular embodiment, the memory 42 is non-volatile memory. In a particular embodiment, the memory 42 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0068] The memory 42 can be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 41.

[0069] The processor 41 implements any of the credit institution recommendation methods described in the above embodiments by reading and executing computer program instructions stored in the memory 42.

[0070] In some embodiments, the computer device may further include a communication interface 43 and a bus 40. For example, Figure 4 As shown, the processor 41, memory 42, and communication interface 43 are connected through bus 40 and complete communication with each other.

[0071] The communication interface 43 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of this application. The communication interface 43 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0072] Bus 40 includes hardware, software, or both, that couples components of a computer device together. Bus 40 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 40 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 40 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0073] Furthermore, in conjunction with the credit institution recommendation methods in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the credit institution recommendation methods in the above embodiments.

[0074] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A recommendation method for credit institutions, characterized in that, include: Obtain the model score of the target user on each credit institution, wherein the model score is used to indicate the probability that the credit institution grants credit to the target user; Obtain the credit correlation among multiple credit institutions, wherein the credit correlation is related to a first matrix and a second matrix, the elements in the first matrix are the model scores of different users in one of the multiple credit institutions, and the elements in the second matrix are the model scores of different users in another of the multiple credit institutions. The multiple credit institutions are classified according to the aforementioned credit relevance; Select a target number of credit institutions from each category to form a target credit institution portfolio and recommend it to the target user.

2. The recommendation method for credit institutions according to claim 1, characterized in that, Obtaining the credit correlation between multiple credit institutions includes: The credit correlation rho(A,B) between credit institution A and credit institution B is calculated using the following formula: Where u=[score(A1), score(A2), score(A3),...,score(An)], v=[score(B1),score(B2), score(B3),...,score(Bn)], where score(An) represents the model score of the nth user on credit institution A, and score(Bn) represents the model score of the nth user on credit institution B.

3. The recommendation method for credit institutions according to claim 1, characterized in that, The classification of the multiple credit institutions according to the aforementioned credit relevance includes: Credit institutions among the multiple credit granting institutions whose pairwise credit correlation exceeds a first value are classified into the first category; Credit institutions whose credit relevance to the remaining credit institutions among the plurality of credit institutions is lower than the second value are classified into the second category; For the remaining credit granting institutions other than the first and second categories among the multiple credit granting institutions, if the credit correlation between the remaining credit granting institutions and more than the target number of credit granting institutions in the first category exceeds the first value, then the remaining credit granting institutions are classified into the first category; otherwise, the remaining credit granting institutions are classified into the third category.

4. The recommendation method for credit institutions according to claim 1, characterized in that, Selecting a target number of credit institutions from each category to form a target portfolio of credit institutions and recommending it to the target user includes: Based on the ranking of the credit granting probability of credit institutions within each category, the target number of credit institutions are selected from each category to form a target credit institution portfolio; The target credit institution portfolio is recommended to the target user.

5. The recommendation method of the credit institution according to any one of claims 1 to 4, characterized in that, After selecting a target number of credit institutions from each category to form a target credit institution portfolio and recommending it to the target user, the process further includes: The overall credit probability p(A,B) of the target user on the target credit institution portfolio is calculated using the following formula, where the target credit institution portfolio includes credit institution A and credit institution B: Where p1 represents the probability of the target user being granted credit by credit institution A, and p2 represents the probability of the target user being granted credit by credit institution B.

6. A recommendation device for a credit institution, characterized in that, include: The first acquisition unit is used to acquire the model score of the target user on each credit institution, wherein the model score is used to indicate the probability that the credit institution grants credit to the target user; The second acquisition unit is used to acquire the credit correlation among multiple credit institutions, wherein the credit correlation is related to a first matrix and a second matrix, the elements in the first matrix are the model scores of different users in one of the multiple credit institutions, and the elements in the second matrix are the model scores of different users in another of the multiple credit institutions. A classification unit is used to classify the plurality of credit institutions according to the credit relevance; The recommendation unit is used to select a target number of credit institutions from each category to form a target credit institution portfolio and recommend it to the target user.

7. The recommendation device for a credit institution according to claim 6, characterized in that, The second acquisition unit includes: The calculation module is used to calculate the credit correlation rho(A,B) between credit institution A and credit institution B according to the following formula: Where u=[score(A1), score(A2), score(A3),...,score(An)], v=[score(B1),score(B2), score(B3),...,score(Bn)], where score(An) represents the model score of the nth user on credit institution A, and score(Bn) represents the model score of the nth user on credit institution B.

8. The recommendation device for a credit institution according to claim 6, characterized in that, The classification unit includes: The first classification module is used to classify credit institutions among the multiple credit granting institutions whose pairwise credit correlation exceeds a first value into the first category; The second classification module is used to classify credit institutions whose credit relevance with the other credit institutions among the plurality of credit institutions is lower than the second value into the second category; The third classification module is used to classify the remaining credit granting institutions (excluding the first and second categories) into the first category if the credit correlation between the remaining credit granting institutions and the credit granting institutions in the first category exceeding the target number exceeds the first value; otherwise, the remaining credit granting institutions are classified into the third category.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the recommendation method of the credit institution as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the recommendation method of the credit institution as described in any one of claims 1 to 5.

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