Credit granting report generation method and related equipment

Abstract

The embodiment of the invention discloses a credit granting report generation method and related equipment, and relates to the technical field of computers. In the embodiment of the specification, a plurality of log data generated by a plurality of systems in a credit granting service providing process are collected; classifying and calculating the plurality of log data to obtain at least one node and node information corresponding to each node; and according to the node information corresponding to the at least one node, performing visualization processing on the at least one node to obtain a credit extension process and a credit extension report including the credit extension process. In other words, in the specification, a complete credit extension process is restored based on a time sequence view of user credit activities through a plurality of log data, a plurality of nodes in the credit extension process are further tracked and integrated into a coherent credit life cycle view, and a credit extension report convenient to check and understand by a manager is generated.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a credit report generation method and related equipment. Background Art

[0002] Currently, financial technology companies and banks are actively promoting the automation of credit approval processes to provide credit services to users. In the credit approval automation process, multiple systems based on data analysis, machine learning, and artificial intelligence are usually used to improve credit efficiency and accuracy. These systems can quickly analyze a large amount of data in the credit approval process and implement the following functions: credit scoring, based on the user's credit report, calculate a comprehensive score; capital verification, verifying the user's financial status through payroll, tax bills, etc., and checking the user's historical loan and repayment records to determine their repayment ability and willingness; behavioral analysis, analyzing the user's consumption, savings, and investment behaviors to identify potential risks.

[0003] Using multiple systems to automate approval when providing credit services can reduce the time for manual review, increase the speed of credit approval, and reduce human errors and biases to a certain extent.

[0004] However, in actual operation, when providing a complete credit service to users, multiple systems need to cooperate in credit approval operations, and each system makes its corresponding part of the credit decision based on its set functions. Therefore, the current credit approval process automation has the problem of confusing credit process and decision-making link records. External auditors or internal managers cannot restore the complete credit approval process, nor can they understand how each decision is made in the entire credit approval process. Summary of the invention

[0005] The embodiments of this specification provide a credit report generation method and related devices, which can solve the above problems. The technical solution is as follows:

[0006] In a first aspect, an embodiment of the present specification provides a method for generating a credit report, the method comprising:

[0007] Collect multiple log data generated by multiple systems in the process of providing credit services;

[0008] Classifying and calculating the plurality of log data to obtain at least one node and node information corresponding to each of the nodes;

[0009] According to the node information respectively corresponding to the at least one node, the at least one node is visualized to obtain a credit granting process and a credit granting report including the credit granting process.

[0010] In a second aspect, an embodiment of the present specification provides a credit report generating device, the device comprising:

[0011] The log collection module is used to collect multiple log data generated by multiple systems in the process of providing credit services;

[0012] A log calculation module, used to classify and calculate the plurality of log data to obtain at least one node and node information corresponding to each of the nodes;

[0013] The report generation module is used to visualize the at least one node according to the node information respectively corresponding to the at least one node, and obtain a credit granting process and a credit granting report including the credit granting process.

[0014] In a third aspect, an embodiment of the present specification provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0015] In a fourth aspect, an embodiment of the present specification provides a computer program product, wherein the computer program product stores a plurality of instructions, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0016] In a fifth aspect, an embodiment of the present specification provides an electronic device, which may include: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0017] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least:

[0018] In an embodiment of the present specification, multiple log data generated by multiple systems in the process of providing credit services are collected. The log data is a direct record of the credit approval process of a certain system, including detailed information on at least one credit event that occurs in the process of providing credit services by a certain system, such as a credit limit increase event, a credit limit reduction event, a thawing event, a withdrawal event, a price increase event, a price reduction event, etc.

[0019] Furthermore, multiple log data are classified and calculated to obtain at least one node and node information corresponding to each node. In this specification, through in-depth analysis and application of multiple log data, the nodes that affect the change of user credit status are accurately captured, and the node information corresponding to the multiple nodes provides a direct historical basis for credit risk assessment of users.

[0020] Furthermore, at least one node is visualized according to the node information corresponding to at least one node, and a credit granting process and a credit granting report including the credit granting process are obtained. The complete credit granting process is restored based on the time series perspective of the user's credit activities through multiple log data, that is, the entire credit granting process from the first application to the approval of loan settlement or default is restored. Multiple nodes in the credit granting process are tracked and integrated into a coherent credit life cycle view, and a credit granting report is generated that is easy for managers to view and understand.

[0021] Based on the credit report including the credit granting process, managers can have a deeper understanding of the actual effect of risk control strategies in the credit granting process and identify which measures are effective and which need to be improved. This provides a solid empirical basis for formulating more accurate credit policies and optimizing risk pricing models set up on multiple systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 It is a schematic diagram of the architecture of a credit report generation method provided in an embodiment of this specification;

[0024] Figure 2 It is a flowchart of a credit report generation method provided in an embodiment of this specification;

[0025] Figure 3 This is a schematic diagram of the architecture of a credit report generation system provided in an embodiment of this specification;

[0026] Figure 4 It is a structural diagram of a credit granting process provided by an embodiment of this specification;

[0027] Figure 5A , Figure 5B This is a schematic diagram of a page for querying a credit report provided in an embodiment of this specification;

[0028] Figure 6 It is a flowchart of a credit report generation method provided in an embodiment of this specification;

[0029] Figure 7 It is a flowchart of a credit report generation method provided in an embodiment of this specification;

[0030] Figure 8This is a schematic diagram of a scenario of querying a target node in a credit granting process provided by an embodiment of this specification;

[0031] Fig. 9 It is a structural diagram of a credit report generating device provided in an embodiment of this specification;

[0032] Fig.10 It is a structural schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0034] In the description of this specification, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In the description of this specification, it should be noted that, unless otherwise clearly specified and limited, "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices. For those of ordinary skill in the art, the specific meanings of the above terms in this specification can be understood in specific circumstances. In addition, in the description of this specification, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are an "or" relationship.

[0035] The present specification is described in detail below with reference to specific embodiments.

[0036] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions. For example, the features, information and data involved in this specification are all obtained with full authorization.

[0037] Risk control, also known as risk management, is an important part of the financial industry, especially in the banking and consumer finance sectors. Risk control refers to identifying, evaluating, monitoring and mitigating various risks that may be encountered in financial activities through a series of strategies, methods, processes and technical means.

[0038] Credit granting refers to a financial institution's commitment to grant a user a loan or credit line within a certain period and within a certain amount limit based on the user's credit status, repayment ability and other factors. The credit granting process involves steps such as assessing the user's credit, determining the credit limit, setting conditions (such as interest rate, repayment method, etc.), and signing a contract.

[0039] Credit refers to all credit-based lending activities, including but not limited to personal loans, corporate loans, credit card services, etc. In the financial field, credit refers to the act of financial institutions providing funds to users and users promising to repay the principal and interest at a certain point in time or time period in the future.

[0040] Currently, financial technology companies and banks are actively promoting the automation of credit approval processes to provide credit services to users. In the credit approval automation process, multiple systems based on data analysis, machine learning, and artificial intelligence are usually used to improve credit efficiency and accuracy. These systems can quickly analyze a large amount of data in the credit approval process and implement the following functions: credit scoring, based on the user's credit report, calculate a comprehensive score; capital verification, verifying the user's financial status through payroll, tax bills, etc., and checking the user's historical loan and repayment records to determine their repayment ability and willingness; behavioral analysis, analyzing the user's consumption, savings, and investment behaviors to identify potential risks.

[0041] Using multiple systems to automate approval when providing credit services can reduce the time for manual review, increase the speed of credit approval, and reduce human errors and biases to a certain extent.

[0042] However, in actual operation, when providing a complete credit service to users, multiple systems need to cooperate in credit approval operations, and each system makes its corresponding part of the credit decision based on its set functions. Therefore, the current credit approval process automation has the problem of confusing credit process and decision-making link records. External auditors or internal managers cannot restore the complete credit approval process, nor can they understand how each decision is made in the entire credit approval process.

[0043] Therefore, the present specification provides a method for generating a credit report to solve the problem. Figure 1 As shown, Figure 1 It is a schematic diagram of the architecture of a credit report generation method provided in an embodiment of this specification. Figure 1It comprises a plurality of electronic devices, which at least include a server 101 for executing a credit report generation method, a plurality of servers for providing credit services and a plurality of electronic devices for viewing credit reports.

[0044] The multiple servers for providing credit services include at least server 1021, server 1022, server 1023 and server 1024, and the multiple electronic devices for viewing credit reports include at least electronic device 1031 and electronic device 1032. It can be understood that Figure 1 The number and types of servers and electronic devices shown in the figure are for illustration only, and the embodiments of this specification do not impose any limitation on this.

[0045] The above-mentioned electronic devices and servers include but are not limited to physical or virtual processors, mobile stations (MobileStation, MS), mobile terminal equipment (Mobile Terminal), mobile phones (Mobile Telephone), handsets and portable equipment (portable equipment), Bluetooth headsets, smart watches and other types. The electronic devices can communicate with one or more core networks via a radio access network (Radio Access Network, RAN).

[0046] For example, the server is a plurality of physical servers, and the plurality of physical servers are independent in hardware. Or, the server is a plurality of virtual servers, and the plurality of virtual servers are deployed in the same hardware resource pool, and the deployment methods of the virtual servers include but are not limited to: VMware, Virtual Box and Virtual PC.

[0047] In the embodiment of the present specification, multiple systems are set on servers such as server 1021, server 1022 and server 1023, which are used to cooperate with each other to provide credit services to a large number of users and realize credit approval automation. Server 101 obtains multiple log data generated by multiple systems in the process of providing credit services through communication connections with servers such as server 1021, server 1022 and server 1023, thereby executing the credit report generation method provided in the embodiment of the present specification to generate a credit report. Electronic devices such as electronic device 1031 and electronic device 1032 are used to obtain credit reports so that managers or auditors can view the credit reports.

[0048] It is understandable that the servers 101, 1021, 1022, and 1023 also have other service capabilities and functions to complete the tasks in the following embodiments. For example, the server 101 also provides portal services, resource management services, and CI / CD services.

[0049] In the embodiments of the present specification, electronic devices such as electronic device 1031 and electronic device 1032 may also be equipped with a display device, and the display device may be any device capable of realizing a display function, for example, the display device may be a cathode ray tube display (CR), a light-emitting diode display (LED), an electronic ink screen, a liquid crystal display (LCD), a plasma display panel (PDP), etc. For example, a user may use the display device on electronic device 1031 to send a request for obtaining a credit report to server 101, or view a credit report sent by server 101.

[0050] Multiple electronic devices and multiple servers can communicate through communication links established by communication protocols, such as the gRPC protocol. gRPC is a high-performance, general-purpose open source remote procedure call (RPC) framework, which is mainly designed for mobile application development and based on the HTTP / 2 protocol standard, based on the protocol buffer (PB) serialization protocol development, and supports many development languages. In addition, the communication link can also be a wireless communication link or a wired communication link, for example, a wired communication link includes an optical fiber, a twisted pair or a coaxial cable, and a wireless communication link includes a Bluetooth communication link, a wireless fidelity (WIreless-FIdelity, Wi-Fi) communication link or a microwave communication link.

[0051] In one embodiment, Figure 2 As shown, it is a schematic diagram of a credit report generation method provided in an embodiment of this specification, which can be implemented by a computer program and can be run on a credit report generation device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0052] Specifically, the credit report generation method includes:

[0053] S102. Collect multiple log data generated by multiple systems in the process of providing credit services.

[0054] Log data is the recorded information generated by multiple systems in the process of executing credit approval, mainly used to record their operating status, user behavior, error events, performance data, etc. In the embodiment of this specification, various log data generated by systems installed on network devices, security devices, hosts, middleware, databases, storage devices, etc. in the process of providing credit services are received.

[0055] Multiple systems can be understood as systems used to provide credit services to users. For example, multiple systems include a risk decision system, which is mainly used to evaluate the credit risk of users who apply for credit, and make decisions on whether to provide credit based on this. For example, multiple systems also include a customer relationship management system CRM, which is used to record information about interactions with users, including historical transactions, customer service requests, and other information. For example, multiple systems include an account management system, which is used to manage customer account information, such as account opening, account closing, account status changes, and the like. For example, multiple systems include a transaction processing system, which is used to process transactions related to user accounts, such as deposits, withdrawals, transfers, and the like. For example, multiple systems include an anti-fraud system, which is used to detect and prevent potential fraudulent behavior. For example, multiple systems include a compliance system, which is used to ensure that each credit decision in the credit service meets regulatory requirements. For example, multiple systems include a data analysis system, which is used to analyze customer behavior patterns, market trends, and the like, to help make better credit decisions. For example, multiple systems include an identity verification system, which is used to confirm the authenticity of the identity of the user who applied for credit.

[0056] like Figure 3 As shown, Figure 3 201 , a risk control decision system 202 , a log storage system 202 , a risk control operation system 203 and a consumer finance system 204 .

[0057] Among them, the risk management decision system 201 (Risk Management Decision System) is a software system specially designed to evaluate and manage credit risks. It uses algorithms, models and business rules to automatically evaluate the credit risk of users, and makes decisions on whether to approve loans based on this. The log storage system 202 (Log Storage System) is a centralized data storage solution used to collect, store and manage multiple log data from multiple systems in the process of providing credit services. The risk management operation system 203 (Risk Management Operation System) is used to support daily risk management and operational tasks. The consumer finance system 204 (Consumer Finance System) is a system designed specifically for consumer finance affairs, which handles credit-related affairs such as loan types, interest rates, terms, etc.

[0058] The process of multiple systems providing credit services to users includes multiple steps. For example, credit services may include the following steps: application reception, where users submit credit applications; document review, to verify the authenticity of the information provided by users, including personal identity information, financial status, etc.; credit assessment, using credit scoring models to assess the credit risk of users; risk pricing, determining loan interest rates or credit card limits based on the results of risk assessment; approval decisions, deciding whether to approve credit applications; post-loan monitoring, continuously monitoring users' repayments and other changes that may affect their credit status during the credit period. Credit services may also include other steps, which are determined by the functions of the system settings involved in this credit service.

[0059] exist Figure 3 In the illustrated embodiment, the user initiates a credit request through a web page 206 and / or an application page 207 set on a user device for applying for a credit service. The credit request is forwarded to multiple servers on which multiple systems are set through a gateway 205. The risk control decision system 201, the risk control operation system 203, and the consumer finance system 204 provide credit services to users based on the credit request. The server, which is the execution subject of the embodiment of this specification, collects multiple log data of the above multiple systems in the process of providing credit services, and stores the multiple log data in the log storage system 202.

[0060] S104: Classify and calculate multiple log data to obtain at least one node and node information corresponding to each node.

[0061] Categorizing multiple log data can be understood as categorizing multiple log data based on a preset categorization method. The categorization method includes categorizing multiple log data according to multiple preset types, each type corresponding to at least one log data. The categorization method can also be splitting each log data into multiple log data segments, that is, obtaining multiple log data segments corresponding to the multiple log data, and further categorizing the multiple log data segments.

[0062] For example, the same credit service is provided to multiple users, and the purpose of the credit service is to increase the credit limits of multiple users. The process of the credit service at least includes reviewing the identity information of each user and increasing the credit limit. When multiple systems provide the same credit service to multiple users, log data corresponding to each user is generated, and the log data corresponding to each user includes a log data segment of the identity information review corresponding to the user and a log data segment of the credit limit increase for the user. Furthermore, the log data segments of the identity information review corresponding to multiple users are classified, and the log data segments of the credit limit increase corresponding to multiple users are classified.

[0063] Furthermore, the classified multiple log data are calculated according to the preset multiple calculation methods to obtain at least one node and the node information corresponding to each node. The node can be understood as a key step in the credit granting process, and the node information represents the information corresponding to the step, which may include any data such as input data, output data, decision data, risk data, etc. of the credit granting step corresponding to the node.

[0064] For example, at least one node includes an application submission node, and the node information may include the application date and time, basic information of the applicant, and the application amount, etc. At least one node also includes a document review node, and the node information corresponding to the node may include the review start time, end time, review result (pass / reject), and rejection reason (if any). At least one node also includes a document review node credit assessment node, and the node information corresponding to the node may include the parameters used by the credit assessment model, the scoring result, and the scoring basis, etc. At least one node also includes an approval decision node, and the node information corresponding to the node may include: approval result (approval / rejection), approval amount (if any), approval date and time, etc. At least one node also includes a loan execution node, and the node information corresponding to the node may include: loan date and time, loan amount, capital flow, etc.

[0065] In one embodiment, different types of log data are preset to correspond to different calculation methods. According to the preset calculation methods corresponding to different types of log data, the plurality of log data are classified and calculated to obtain at least one node and the node information corresponding to each of the nodes.

[0066] Different types of log data may include log data generated based on different credit events, log data generated based on different systems, and log data generated by completing different credit granting steps in multiple credit granting steps included in a complete credit granting service. According to the type of log data, the calculation method corresponding to each type of log data is determined.

[0067] In one embodiment, multiple systems include a risk control decision system. Multiple credit events occur in the risk control decision system during the process of providing credit services. The multiple credit events include at least one of the following credit events: user information query event, credit limit increase event, credit limit reduction event, thawing event, clearance event, price increase event, and price reduction event.

[0068] The Risk Management Decision System is a software system specially designed to assess and manage credit risk. It uses algorithms, models and business rules to automatically assess the credit risk of users and make decisions on whether to approve loans based on this. Therefore, the Risk Management Decision System generates multiple credit events in the process of providing credit services. The multiple credit events include at least one of the following credit events: user information query event, credit limit increase event, credit limit reduction event, unfreeze event, withdrawal event, price increase event, price reduction event. In other words, at least one of the above credit events will occur in the process of the Risk Management Decision System providing credit services to users.

[0069] In this embodiment, multiple log data generated by the risk control decision system based on multiple credit events in the process of providing credit services are collected. In other words, multiple credit events occur in the process of the risk control decision system providing credit services to at least one user, and the risk control decision system mobilizes the devices set in the system to execute the credit events to generate log data corresponding to the credit events. For example, in the process of the risk control decision system providing credit services to users, a user information query event, a credit limit increase event, and a price increase event occur. The multiple log data generated by the risk control decision system during this credit service include log data corresponding to the user information query event, the credit limit increase event, and the price increase event, respectively.

[0070] Furthermore, multiple log data are classified to obtain at least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information. Before the multiple log data are classified and calculated, it is necessary to perform cleaning calculations on the log data to remove noise data (such as redundant information, erroneous formats, etc.) in the log data and convert the log data into a unified and easy-to-analyze format. For example, cleaning the log data includes standardization, deduplication, parsing, etc.

[0071] Furthermore, multiple log data are classified to obtain at least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information. Among them, input data can be understood as the original request or user behavior data before entering the risk control decision system. Output data can be understood as the result after the risk control decision system makes a decision, such as whether to allow the transaction, the rating level, etc. Intermediate information can be understood as temporary data generated in the process of the risk control decision system providing credit services, such as risk scores, rule matching results, etc.

[0072] According to the preset calculation methods corresponding to different types of log data, at least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information are calculated respectively to obtain at least one node and node information corresponding to each of the nodes.

[0073] In this embodiment, taking the multiple log data generated by the risk control decision system in the process of providing credit services based on multiple credit events as an example, the multiple log data are classified to obtain log data corresponding to input information, output information and intermediate information respectively, and further the above different types of log data are calculated according to the preset calculation methods corresponding to different types of log data respectively, to obtain at least one node and the node information corresponding to each of the nodes. The method provided in this embodiment can improve the accuracy of the types and quantities of the obtained nodes, and improve the accuracy of the node information corresponding to the obtained nodes.

[0074] S106. Visualize at least one node according to the node information corresponding to the at least one node, and obtain a credit granting process and a credit granting report including the credit granting process.

[0075] Collect and process the node information corresponding to each node, which also includes the node metadata, that is, the descriptive information of each node, such as node type, execution time, processing status, etc. Visualize at least one node through the node information corresponding to each node to obtain a credit process that records the credit request as the starting node to the end node as the credit completion. The credit process is used to display multiple credit steps included in the credit approval process in the entire credit service.

[0076] like Figure 4 As shown, Figure 4It is a structural diagram of a credit granting process provided in an embodiment of this specification. The credit granting service corresponding to the credit granting process is to increase the credit limit of a certain user. Specifically, the credit granting process of this embodiment includes a human review step as node 301 and a human review protection step as node 302. In the credit approval process, "human review" refers to the review process performed manually. Unlike automated approval (such as automatic processing through algorithms, rule engines, etc.), human review involves a credit specialist or approval personnel conducting a comprehensive review of the applicant's information and situation to make a credit decision. The human review process is usually used to deal with complex situations, high-risk applications, or situations that require more detailed judgment.

[0077] Furthermore, it is determined whether this credit application requires human review protection. Specifically, an exclusive gateway is used to detect whether the application hits the human review protection rules. If it is determined that this credit application requires human review protection, the automated approval process is terminated. If it is determined that this credit application does not require human review protection, the user information query step is entered as node 303 in the credit process.

[0078] After obtaining user information, the automated approval process is performed through multiple parallel credit events as node 304 in the credit process. Specifically, in this embodiment, multiple parallel credit events at least include anti-sanctions, obtaining unfreezing features, confirming withdrawal, obtaining smart credit features, etc. Further, after processing the above multiple parallel credit events, the pre-increase step as node 305 is entered, and the user's credit is synchronously or asynchronously queried through an exclusive gateway, where the step of synchronously querying credit is node 306 in the credit process.

[0079] Furthermore, based on a plurality of log data, it can be determined that the user meets the preconditions for credit limit increase, and the credit authorization process also includes a step of successful credit limit increase as node 307 . Figure 4 The credit granting process shown represents a credit granting service for increasing the credit limit of a user, and Figure 4 The credit approval process shown is the result of visualizing multiple nodes, which allows managers to intuitively review multiple nodes in the entire credit approval process.

[0080] In the embodiment of the present specification, a credit report including the credit process is generated according to the credit process. The credit report specifically includes analysis information of the credit process, such as risk assessments corresponding to multiple credit decisions involved in the entire credit process, analysis of the reasons for credit failure, etc. In another embodiment, the credit report can also be analysis information of a certain node in the credit process, that is, analysis information of a certain credit step or a certain risk control decision in the entire credit process.

[0081] like Figure 5A , Figure 5B As shown, Figure 5A , Figure 5BIt is a schematic diagram of a page for querying a credit report provided in the embodiment of this specification. It can be understood that Figure 5A , Figure 5B The format and content of the credit report shown are for reference only and this instruction manual does not impose any restrictions on this.

[0082] exist Figure 5A In the embodiment shown, the manager 401 sends a credit report query request to the server executing the credit report generation method through an electronic device, and the content of the credit report query request is "query list 30987024096". Among them, 30987024096 is the identifier of a credit step in a credit process, that is, a risk control decision. The server receives the credit report query request and sends a credit report to the electronic device corresponding to the manager 401. Figure 5A As shown, the credit report includes information such as the decision ID, decision version, decision time, whether the decision is passed, and the credit limit involved in the decision.

[0083] exist Figure 5B In the embodiment shown, the manager 402 sends a credit report query request to the server executing the credit report generation method through an electronic device. The content of the credit report query request is "309884218312 Risk control rejection reason". Among them, 309884218312 is the user corresponding to the credit service. The server receives the credit report query request and sends a credit report to the electronic device corresponding to the manager 401. Figure 5B As shown, the credit report includes the tracking identifier TraceIdentifier, decision time, decision ID, decision result (including the decision version used for this decision, risk index, basic region information RR_BASIC_REGION used in the system to process risk control requests and approval status), and the rejection reason that caused the decision result to fail.

[0084] In an embodiment of the present specification, multiple log data generated by multiple systems in the process of providing credit services are collected. The log data is a direct record of the credit approval process of a certain system, including detailed information on at least one credit event that occurs in the process of providing credit services by a certain system, such as a credit limit increase event, a credit limit reduction event, a thawing event, a withdrawal event, a price increase event, a price reduction event, etc.

[0085] Furthermore, multiple log data are classified and calculated to obtain at least one node and node information corresponding to each node. In this specification, through in-depth analysis and application of multiple log data, the nodes that affect the change of user credit status are accurately captured, and the node information corresponding to the multiple nodes provides a direct historical basis for credit risk assessment of users.

[0086] Furthermore, at least one node is visualized according to the node information corresponding to at least one node, and a credit granting process and a credit granting report including the credit granting process are obtained. The complete credit granting process is restored based on the time series perspective of the user's credit activities through multiple log data, that is, the entire credit granting process from the first application to the approval of loan settlement or default is restored. Multiple nodes in the credit granting process are tracked and integrated into a coherent credit life cycle view, and a credit granting report is generated that is easy for managers to view and understand.

[0087] Based on the credit report including the credit granting process, managers can have a deeper understanding of the actual effect of risk control strategies in the credit granting process and identify which measures are effective and which need to be improved. This provides a solid empirical basis for formulating more accurate credit policies and optimizing risk pricing models set up on multiple systems.

[0088] In one embodiment, Figure 6 As shown, it is a schematic diagram of a credit report generation method provided in an embodiment of this specification, which can be implemented by a computer program and can be run on a credit report generation device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0089] Specifically, the credit report generation method includes:

[0090] S202: Obtain a standard log model, and modify extended parameters in the standard log model according to the type of credit service provided by each system to obtain a log model corresponding to each system.

[0091] The standard log model is used to generate log data in a standard format. The standard format obtained by the log model includes core fields that are generally applicable in the credit approval process. For example, log data in a standard format usually includes the following core fields: Timestamp, used to record the time when the log data was generated; Log level, used to indicate the importance of the log, such as INFO, WARNING, ERROR, etc.; Source System ID, used to identify the system that generated the log data; Operation Type, describing the type of credit step, such as query, approval, update, etc.; User ID, used to record the user ID of the credit application; Status Code, indicating the result status of the risk control decision, such as success, failure, etc.

[0092] According to the type of credit service provided by each system, the standard log model needs to be modified, specifically the extended parameters in the standard log model are modified, so that the log data obtained through the modified log model can record more information specific to the credit service provided by the system. In other words, each system is set with a log model with extended parameters modified based on the type of credit service provided by the system, and the log data formats generated by the log models set on different systems are different.

[0093] The extended parameters of the standard log model may include extended parameters of the credit application log, extended parameters of the credit approval log, extended parameters of the credit issuance log, and extended parameters of the credit repayment log. In the centralized model, the extended parameters of the credit application log include parameters such as applicant information and credit score. The extended parameters of the credit approval log include parameters such as approval opinion and risk score. The extended parameters of the credit issuance log include parameters such as credit amount, account information, and transaction status. The extended parameters of the credit repayment log include parameters such as repayment amount and repayment time.

[0094] S204. Collect multiple log data generated by multiple systems in the process of providing credit services according to the log models set in each system.

[0095] The format of the log data generated by each system is determined by the log model corresponding to the system, and the format of the log data generated by the log models set on different systems is different. According to the log model set in the system, multiple log data generated in the process of providing credit services by the system have the same format as the multiple log data corresponding to the system.

[0096] In one embodiment, initial decision log data generated by a decision engine included in the system is collected during the process of providing credit services; multiple initial decision log data are desensitized to obtain multiple decision log data corresponding to the multiple initial decision log data; wherein the multiple log data include multiple decision log data.

[0097] The decision engine is an important part of the credit approval system. It evaluates the credit risk of applicants based on preset rules, models and algorithms, and generates preliminary approval decisions. The log model records the decision engine when it makes a decision, and obtains the initial decision log data.

[0098] Furthermore, the initial decision log data is desensitized. The main purpose of desensitization is to protect the security of personal information and avoid the leakage of sensitive information such as personal privacy data. Desensitization can include masking, encryption, hashing, randomization and other processing methods. After desensitization, the initial decision log data becomes the corresponding decision log data. The decision log data still retains the key information of the decision, but removes the sensitive part to ensure the security and compliance of the log data.

[0099] In another embodiment, the initial process engine included in each system is transformed to obtain the process engine included in each system; and process log data generated by the process engine included in the system during the process of providing credit services is collected, wherein the process log data includes metadata of at least one target node in the system process generated by the process engine, as well as metadata of upstream nodes and downstream nodes corresponding to the target node, and the multiple log data include multiple process log data.

[0100] In this embodiment, the initial process engine can be understood as a transaction process management tool initially used in the system, which is used to define the various steps of credit approval and ensure that these steps can be executed according to a predetermined order and rules.

[0101] The initial process engines included in each system are transformed to obtain a process engine, which mainly adds a process step when executing the system process based on the process engine to complete the credit process. The process step is used to print the metadata of the upstream and downstream nodes of the node when processing the target node of the system process.

[0102] It is understandable that the above-mentioned system process and credit process are not the same. The credit process is a complete credit approval process determined based on the log data corresponding to multiple systems, and the system process is a process executed by the process engine of each system.

[0103] Metadata is used to describe and illustrate the node itself of at least one target node in the system process generated by the process engine. For example, the metadata of a node includes timestamp, node number, node name, node status, etc. The process log data also includes metadata of the upstream node and downstream node corresponding to the target node in the system process, and the number of target nodes is one or more.

[0104] In this embodiment, the process log data generated by the process engine is collected, especially the metadata of the target node and its upstream and downstream nodes in the system process is used as the process log data, which can enrich the content of the log data including the process log data, and provide a detailed basis for the subsequent data analysis, troubleshooting and transaction optimization of the credit process determined by the log data, thereby improving the transparency and traceability of multiple systems providing credit services to achieve credit approval automation.

[0105] S206: According to a preset index configuration strategy, configure an index mode corresponding to each log data for the multiple log data.

[0106] The preset index configuration strategy includes but is not limited to: index naming rules, for example, using a date prefix to name the index; index lifecycle management, which is used to define the lifecycle of the index, including operations such as creation, deletion, and archiving; field mapping, which defines which fields in the log data need to be indexed; sharding and replica settings, which define the number of shards and replicas of the index to improve retrieval performance and data reliability.

[0107] Configure the index mode according to the characteristics of the format of each log data. Since the extended parameters of the log model have been modified, the format of the log data generated by each system is related to the log module set on the system. Therefore, different index modes are configured based on log data of different formats. For example, different index modes are configured for each log data according to the field name, data type, system name, decision condition, decision parameter, etc. included in the log data of different formats.

[0108] S208: storing the configured multiple log data in a log storage system.

[0109] The configured multiple log data are stored in the log storage system so as to generate a credit report based on the multiple log data. In this embodiment, through SLS (Serverless Logging Service) log query, different index modes are configured for different log data, and the index method is optimized, so that the multiple log data required for generating the credit report can be quickly queried, thereby improving the efficiency of generating the credit report.

[0110] S210, classify and calculate multiple log data to obtain at least one node and node information corresponding to each node. See the above S104, which will not be repeated here.

[0111] S212. Visualize at least one node according to the node information corresponding to the at least one node, and obtain a credit granting process and a credit granting report including the credit granting process.

[0112] See the above S106, which will not be repeated here.

[0113] In an embodiment of the present specification, multiple log data generated by multiple systems in the process of providing credit services are collected. The log data is a direct record of the credit approval process of a certain system, including detailed information of at least one credit event that occurs in the process of providing credit services by a certain system. Further, multiple log data are classified and calculated to obtain at least one node and the node information corresponding to each node. In other words, through the in-depth analysis and application of multiple log data, the nodes that affect the change of the user's credit status are accurately captured. Further, according to the node information corresponding to at least one node, at least one node is visualized to obtain a credit process and a credit report including the credit process. Based on the time series perspective of the user's credit activities, the complete credit process is restored through multiple log data, multiple nodes in the credit process are tracked and integrated into a coherent credit life cycle view, and a credit report is generated that is easy for managers to view and understand.

[0114] Based on the credit report including the credit granting process, managers can have a deeper understanding of the actual effect of risk control strategies in the credit granting process and identify which measures are effective and which need to be improved. This provides a solid empirical basis for formulating more accurate credit policies and optimizing risk pricing models set up on multiple systems.

[0115] In one embodiment, Figure 7 As shown, it is a schematic diagram of a credit report generation method provided in an embodiment of this specification, which can be implemented by a computer program and can be run on a credit report generation device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0116] Specifically, the credit report generation method includes:

[0117] S302. Collect multiple log data generated by multiple systems in the process of providing credit services.

[0118] See the above S102, which will not be described again here.

[0119] S304: Classify and calculate multiple log data to obtain at least one node and node information corresponding to each node.

[0120] See the above S104, which will not be described again here.

[0121] S306. Visualize at least one node according to the node information corresponding to at least one node to obtain a credit process, as well as a credit status change result and a credit risk assessment result corresponding to each node in the credit process.

[0122] The credit status change result represents the credit status change of the user corresponding to each node in the credit process. For example, the credit status change result corresponding to the node represents a reduction in the user's credit limit, or the credit status change result corresponding to the node represents an increase in the user's credit limit, or the credit status change result corresponding to the node is that the credit approval result of this credit approval is that 80% of the users are not approved, or the credit status change result corresponding to the node is that the identity information of 60% of the users is unqualified as a result of this identity query.

[0123] The credit risk assessment results represent the risk assessment of the risk control decision corresponding to each node in the credit process. The credit risk assessment results may include the credit risk level, the risk level divided according to the credit score and other factors, such as "low risk", "medium risk" and "high risk", and may also include the risk score and risk assessment opinion of the auditor on the risk control decision corresponding to this node.

[0124] S308. Generate a credit report including the credit process according to the credit status change results and credit risk assessment results corresponding to each node in the credit process.

[0125] Based on the credit status change results and credit risk assessment results corresponding to each node in the credit process, the credit process is displayed using visual forms such as charts and flow charts, and at least one node in the credit process is associated with the credit status change results and credit risk assessment results corresponding to the node by hiding or directly displaying them.

[0126] In this embodiment, the credit process not only includes the node information of each node, but also includes the credit status change results and credit risk assessment results of key nodes. Managers can intuitively understand the credit status changes and risk assessment information corresponding to key risk control decisions in the credit process, ensure the transparency and traceability of the process, and lower the threshold for managers to understand the credit process.

[0127] In one embodiment, a credit report is displayed through an interactive query interface, and a user's query instruction for a target node in a credit process included in the credit report is received through the interactive query interface; in response to the query instruction, the credit status change result and credit risk assessment result corresponding to the target node are displayed.

[0128] The interactive query interface has the functions of displaying credit, reporting received query instructions and displaying query results. Specifically, it displays node information of multiple nodes and at least one key node included in the credit process, receives query instructions from users to select or input specific target nodes for query, and dynamically displays the credit status change results and credit risk assessment results of the target nodes in response to the query instructions.

[0129] Figure 8It is a scene diagram of a target node in a credit granting process provided by an embodiment of this specification. The credit granting process includes multiple nodes as shown in the figure. A query instruction for node 501 is received through an interactive query page. In response to the query instruction, the node information as the admission decision of node 501 is displayed through a dynamic display page, and the node information includes the credit status change result and credit risk assessment result corresponding to node 501.

[0130] In this embodiment, the credit report is displayed through an interactive query interface, and the user's query instructions for the target node are received to display the credit status change results and credit risk assessment results corresponding to the target node. This allows users to quickly understand the node information of key nodes in the credit process, especially the credit status change results and credit risk assessment results obtained based on the node information, thereby improving the practicality of the credit report.

[0131] In an embodiment of the present specification, multiple log data generated by multiple systems in the process of providing credit services are collected. The log data is a direct record of the credit approval process of a certain system, including detailed information of at least one credit event that occurs in the process of providing credit services by a certain system. Further, multiple log data are classified and calculated to obtain at least one node and the node information corresponding to each node. In other words, through the in-depth analysis and application of multiple log data, the nodes that affect the change of the user's credit status are accurately captured. Further, according to the node information corresponding to at least one node, at least one node is visualized to obtain a credit process and a credit report including the credit process. Based on the time series perspective of the user's credit activities, the complete credit process is restored through multiple log data, multiple nodes in the credit process are tracked and integrated into a coherent credit life cycle view, and a credit report is generated that is easy for managers to view and understand.

[0132] Based on the credit report including the credit granting process, managers can have a deeper understanding of the actual effect of risk control strategies in the credit granting process and identify which measures are effective and which need to be improved. This provides a solid empirical basis for formulating more accurate credit policies and optimizing risk pricing models set up on multiple systems.

[0133] The following are device embodiments of this specification, which can be used to implement the method embodiments of this specification. For details not disclosed in the device embodiments of this specification, please refer to the method embodiments of this specification.

[0134] See also Fig. 9, which shows a schematic diagram of the structure of a credit report generation device provided by an exemplary embodiment of this specification. The credit report generation device can be implemented as all or part of the device through software, hardware or a combination of both. The device includes a log collection module 601, a log calculation module 602 and a report generation module 603.

[0135] The log collection module 601 is used to collect multiple log data generated by multiple systems in the process of providing credit services;

[0136] The log calculation module 602 is used to classify and calculate the multiple log data to obtain at least one node and node information corresponding to each of the nodes;

[0137] The report generation module 603 is used to perform visualization processing on the at least one node according to the node information respectively corresponding to the at least one node, and obtain a credit granting process and a credit granting report including the credit granting process.

[0138] In one or more embodiments, the credit report generating device further includes:

[0139] A log model module, used to obtain a standard log model, and to modify the extended parameters in the standard log model according to the type of credit service provided by each system, so as to obtain a log model corresponding to each system; wherein the standard log model is used to generate log data in a standard format;

[0140] The log collection module 601 includes:

[0141] The log collection unit is used to collect multiple log data generated by multiple systems in the process of providing credit services according to the log models set in each of the systems; wherein the format of the log data generated by each of the systems is determined by the log model corresponding to the system.

[0142] In one or more embodiments, the log collection module 601 includes:

[0143] A first collecting unit, used to collect initial decision log data generated by a decision engine included in the system during the process of providing credit granting services;

[0144] The second collection unit is used to perform desensitization processing on the multiple initial decision log data to obtain multiple decision log data corresponding to the multiple initial decision log data; wherein the multiple log data include the multiple decision log data.

[0145] In one or more embodiments, the log collection module 601 further includes:

[0146] A third collecting unit is used to transform the initial process engine included in each of the systems to obtain the process engine included in each of the systems;

[0147] A fourth collection unit is used to collect process log data generated by a process engine included in the system when the system provides credit services; wherein the process log data includes metadata of at least one target node in the system process generated by the process engine, as well as metadata of upstream nodes and downstream nodes corresponding to the target node of the system process, and the multiple log data include multiple process log data.

[0148] In one or more embodiments, the credit report generating device further includes:

[0149] An index configuration module, configured to configure an index mode corresponding to each of the log data for the plurality of log data according to a preset index configuration strategy;

[0150] The log storage module is used to store the configured multiple log data into the log storage system.

[0151] In one or more embodiments, the multiple systems include a risk control decision system, and the risk control decision system generates multiple credit events in the process of providing credit services, and the multiple credit events include at least one of the following credit events: user information query event, credit limit increase event, credit limit reduction event, unfreeze event, withdrawal event, price increase event, price reduction event;

[0152] The log collection module 601 includes:

[0153] An event collection unit, used to collect a plurality of log data generated by the risk control decision system based on the plurality of credit granting events in the process of providing credit granting services;

[0154] The log calculation module 602 includes:

[0155] A log classification unit, configured to classify the plurality of log data to obtain at least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information;

[0156] The log calculation unit is used to calculate at least one log data representing input information, at least one log data representing output information and at least one log data representing intermediate information respectively, to obtain at least one node and node information corresponding to each of the nodes.

[0157] In one or more embodiments, the report generation module 603 includes:

[0158] A visualization processing unit, configured to perform visualization processing on the at least one node according to the node information respectively corresponding to the at least one node to obtain a credit process, and a credit status change result and a credit risk assessment result corresponding to each node in the credit process;

[0159] The report generating unit is used to generate a credit report including the credit process according to the credit status change result and credit risk assessment result corresponding to each node in the credit process.

[0160] In one or more embodiments, the credit report generating device further includes:

[0161] An interactive query module, used to display the credit report through an interactive query interface, and receive a user's query instruction for a target node in the credit process included in the credit report through the interactive query interface;

[0162] The result display module is used to respond to the query instruction and display the credit status change result and credit risk assessment result corresponding to the target node of the credit process.

[0163] In one or more embodiments, the log calculation module 602 includes:

[0164] The classification and calculation unit is used to classify and calculate the multiple log data according to the preset calculation methods corresponding to different types of log data, so as to obtain at least one node and the node information corresponding to each of the nodes.

[0165] In an embodiment of the present specification, multiple log data generated by multiple systems in the process of providing credit services are collected. The log data is a direct record of the credit approval process of a certain system, including detailed information of at least one credit event that occurred in the process of providing credit services by a certain system. The multiple log data are further classified and calculated to obtain at least one node and the node information corresponding to each node. In other words, through the in-depth analysis and application of multiple log data, the nodes that affect the changes in the user's credit status are accurately captured. Furthermore, according to the node information corresponding to at least one node, at least one node is visualized to obtain a credit process and a credit report including the credit process. Based on the time series perspective of the user's credit activities, the complete credit process is restored through multiple log data, multiple nodes in the credit process are tracked and integrated into a coherent credit life cycle view, and a credit report that is easy for managers to view and understand is generated.

[0166] Based on the credit report including the credit granting process, managers can have a deeper understanding of the actual effect of risk control strategies in the credit granting process and identify which measures are effective and which need to be improved. This provides a solid empirical basis for formulating more accurate credit policies and optimizing risk pricing models set up on multiple systems.

[0167] It should be noted that the credit report generation device provided in the above embodiment only uses the division of the above functional modules as an example when executing the credit report generation method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the credit report generation device provided in the above embodiment and the credit report generation method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0168] The serial numbers of the embodiments of this specification are for description only and do not represent the advantages or disadvantages of the embodiments.

[0169] The present specification also provides a computer storage medium, which can store multiple instructions, and the instructions are suitable for being loaded and executed by a processor as described above. Figure 1 - Figure 8 The credit report generation method of the embodiment shown in the figure can be found in the specific execution process. Figure 1 - Figure 8 The specific description of the illustrated embodiment will not be repeated here.

[0170] The present specification also provides a computer program product, which stores at least one instruction, and the at least one instruction is loaded and executed by the processor as described above. Figure 1 - Figure 8 The credit report generation method of the embodiment shown in the figure can be found in the specific execution process. Figure 1 - Figure 8 The specific description of the illustrated embodiment will not be repeated here.

[0171] See also Fig.10 , is a schematic diagram of the structure of an electronic device provided in the embodiment of this specification. Fig.10 As shown, the electronic device 700 may include: at least one processor 701 , at least one network interface 704 , a user interface 703 , a memory 705 , and at least one communication bus 702 .

[0172] The communication bus 702 is used to realize the connection and communication between these components.

[0173] The user interface 703 may include a display screen (Display) and a camera (Camera), and the optional user interface 703 may also include a standard wired interface and a wireless interface.

[0174] The network interface 704 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0175] Among them, the processor 701 may include one or more processing cores. The processor 701 uses various interfaces and lines to connect various parts within the entire server 700, and executes various functions and processes data of the server 700 by running or executing instructions, programs, code sets or instruction sets stored in the memory 705, and calling data stored in the memory 705. Optionally, the processor 701 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 701 can integrate one or more combinations of processors (Central Processing Unit, CPU), image processors (Graphics Processing Unit, GPU) and modems. Among them, the CPU mainly processes operating systems, user interfaces, and applications; the GPU is responsible for rendering and drawing the content that needs to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 701, and it can be implemented separately through a chip.

[0176] Among them, the memory 705 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 705 includes a non-transitory computer-readable storage medium. The memory 705 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 705 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 705 may optionally be at least one storage device located away from the aforementioned processor 701. As Fig.10 As shown, the memory 705 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a credit report generation application.

[0177] exist Fig.10 In the electronic device 700 shown, the user interface 703 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 701 can be used to call the credit report generation application stored in the memory 705 and specifically perform the following operations:

[0178] Collect multiple log data generated by multiple systems in the process of providing credit services;

[0179] Classifying and calculating the plurality of log data to obtain at least one node and node information corresponding to each of the nodes;

[0180] According to the node information respectively corresponding to the at least one node, the at least one node is visualized to obtain a credit granting process and a credit granting report including the credit granting process.

[0181] In one or more embodiments, before the processor 701 executes the step of collecting a plurality of log data generated by a plurality of systems in the process of providing a credit service, the processor 701 further executes:

[0182] Obtaining a standard log model, and modifying the extended parameters in the standard log model according to the type of credit service provided by each system, to obtain a log model corresponding to each system; wherein the standard log model is used to generate log data in a standard format;

[0183] The processor 701 executes the collection of multiple log data generated by multiple systems in the process of providing credit services, specifically performing:

[0184] According to the log model set in each of the systems, a plurality of log data generated by the plurality of systems in the process of providing credit services are collected; wherein the format of the log data generated by each of the systems is determined by the log model corresponding to the system.

[0185] In one or more embodiments, the processor 701 executes the collecting of multiple log data generated by multiple systems in the process of providing credit services, specifically performing:

[0186] Collecting initial decision log data generated by a decision engine included in the system during the process of providing credit granting services;

[0187] Desensitizing the multiple initial decision log data to obtain multiple decision log data corresponding to the multiple initial decision log data; wherein the multiple log data include the multiple decision log data.

[0188] In one or more embodiments, the processor 701 executes the collecting of multiple log data generated by multiple systems in the process of providing the credit service, and further executes:

[0189] Transforming the initial process engine included in each of the systems to obtain the process engine included in each of the systems;

[0190] Collect process log data generated by a process engine included in the system when the system provides credit services; wherein the process log data includes metadata of at least one target node in the system process generated by the process engine, as well as metadata of upstream nodes and downstream nodes corresponding to the target node, and the multiple log data include multiple process log data.

[0191] In one or more embodiments, after the processor 701 executes the step of collecting a plurality of log data generated by a plurality of systems in the process of providing a credit service, the processor 701 further executes:

[0192] According to a preset index configuration strategy, an index mode corresponding to each of the log data is configured for the multiple log data;

[0193] The configured multiple log data are stored in the log storage system.

[0194] In one or more embodiments, the multiple systems include a risk control decision system, and the risk control decision system generates multiple credit events in the process of providing credit services, and the multiple credit events include at least one of the following credit events: user information query event, credit limit increase event, credit limit reduction event, unfreeze event, withdrawal event, price increase event, price reduction event;

[0195] The processor 701 executes the collection of multiple log data generated by multiple systems in the process of providing credit services, specifically performing:

[0196] Collecting a plurality of log data generated by the risk control decision system based on the plurality of credit granting events in the process of providing credit granting services;

[0197] The processor 701 performs the classification and calculation of the plurality of log data to obtain at least one node and the node information corresponding to each of the nodes, specifically performing:

[0198] Classifying the plurality of log data to obtain at least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information;

[0199] At least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information are calculated respectively to obtain at least one node and node information corresponding to each of the nodes.

[0200] In one or more embodiments, the processor 701 performs the visualization processing of the at least one node according to the node information respectively corresponding to the at least one node, and obtains the credit granting process and the credit granting report including the credit granting process, specifically performing:

[0201] According to the node information respectively corresponding to the at least one node, the at least one node is visualized to obtain a credit process, and a credit status change result and a credit risk assessment result corresponding to each node in the credit process;

[0202] A credit report including the credit process is generated according to the credit status change results and credit risk assessment results corresponding to each node in the credit process.

[0203] In one or more embodiments, after the processor 701 performs the visualization processing of the at least one node according to the node information respectively corresponding to the at least one node, and obtains the credit granting process and the credit granting report including the credit granting process, it further performs:

[0204] Displaying the credit report through an interactive query interface, and receiving a user's query instruction for a target node in the credit process included in the credit report through the interactive query interface;

[0205] In response to the query instruction, the credit status change result and credit risk assessment result corresponding to the target node are displayed.

[0206] In one or more embodiments, the processor 701 classifies and calculates the multiple log data to obtain at least one node and node information corresponding to each of the nodes, specifically performing:

[0207] According to the preset calculation methods corresponding to different types of log data, the multiple log data are classified and calculated to obtain at least one node and node information corresponding to each of the nodes.

[0208] In an embodiment of the present specification, multiple log data generated by multiple systems in the process of providing credit services are collected. The log data is a direct record of the credit approval process of a certain system, including detailed information of at least one credit event that occurs in the process of providing credit services by a certain system. Further, multiple log data are classified and calculated to obtain at least one node and the node information corresponding to each node. In other words, through the in-depth analysis and application of multiple log data, the nodes that affect the change of the user's credit status are accurately captured. Further, according to the node information corresponding to at least one node, at least one node is visualized to obtain a credit process and a credit report including the credit process. Based on the time series perspective of the user's credit activities, the complete credit process is restored through multiple log data, multiple nodes in the credit process are tracked and integrated into a coherent credit life cycle view, and a credit report is generated that is easy for managers to view and understand.

[0209] Based on the credit report including the credit granting process, managers can have a deeper understanding of the actual effect of risk control strategies in the credit granting process and identify which measures are effective and which need to be improved. This provides a solid empirical basis for formulating more accurate credit policies and optimizing risk pricing models set up on multiple systems.

[0210] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0211] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0212] The above disclosure is only the preferred embodiment of this specification, which certainly cannot be used to limit the scope of rights of this specification. Therefore, equivalent changes made according to the claims of this specification are still within the scope covered by this specification.

Claims

1. A method for generating a credit report, the method comprising: Collect multiple log data generated by multiple systems in the process of providing credit services; Classifying and calculating the plurality of log data to obtain at least one node and node information corresponding to each of the nodes; According to the node information respectively corresponding to the at least one node, the at least one node is visualized to obtain a credit granting process and a credit granting report including the credit granting process.

2. The credit report generation method according to claim 1, before collecting a plurality of log data generated by a plurality of systems in the process of providing credit services, further comprising: Obtaining a standard log model, and modifying the extended parameters in the standard log model according to the type of credit service provided by each system, to obtain a log model corresponding to each system; wherein the standard log model is used to generate log data in a standard format; The collection of multiple log data generated by multiple systems in the process of providing credit services includes: According to the log model set in each of the systems, a plurality of log data generated by the plurality of systems in the process of providing credit services are collected; wherein the format of the log data generated by each of the systems is determined by the log model corresponding to the system.

3. The credit report generation method according to claim 1, wherein the collecting of multiple log data generated by multiple systems in the process of providing credit services comprises: Collecting initial decision log data generated by a decision engine included in the system during the process of providing credit granting services; Desensitizing the multiple initial decision log data to obtain multiple decision log data corresponding to the multiple initial decision log data; wherein the multiple log data include the multiple decision log data.

4. The credit report generation method according to claim 3, wherein the collecting of multiple log data generated by multiple systems in the process of providing credit services further comprises: Transforming the initial process engine included in each of the systems to obtain the process engine included in each of the systems; Collect process log data generated by a process engine included in the system when the system provides credit services; wherein the process log data includes metadata of at least one target node in the system process generated by the process engine, and metadata of upstream nodes and downstream nodes corresponding to the target node of the system process, and the multiple log data include multiple process log data.

5. The credit report generation method according to claim 1, after collecting a plurality of log data generated by a plurality of systems in the process of providing credit services, further comprising: According to a preset index configuration strategy, an index mode corresponding to each of the log data is configured for the multiple log data; The configured multiple log data are stored in the log storage system.

6. According to the credit report generation method of claim 1, the multiple systems include a risk control decision system, and the risk control decision system generates multiple credit events in the process of providing credit services, and the multiple credit events include at least one of the following credit events: user information query event, credit limit increase event, credit limit reduction event, unfreeze event, withdrawal event, price increase event, price reduction event; The collection of multiple log data generated by multiple systems in the process of providing credit services includes: Collecting a plurality of log data generated by the risk control decision system based on the plurality of credit granting events in the process of providing credit granting services; The classifying and calculating the plurality of log data to obtain at least one node and node information corresponding to each of the nodes includes: Classifying the plurality of log data to obtain at least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information; At least one log data representing input information, at least one log data representing output information, and at least one log data representing intermediate information are calculated respectively to obtain at least one node and node information corresponding to each of the nodes.

7. The credit report generation method according to claim 1, wherein the step of visualizing the at least one node according to the node information respectively corresponding to the at least one node to obtain a credit process and a credit report including the credit process comprises: According to the node information respectively corresponding to the at least one node, the at least one node is visualized to obtain a credit process, and a credit status change result and a credit risk assessment result corresponding to each node in the credit process; A credit report including the credit process is generated according to the credit status change results and credit risk assessment results corresponding to each node in the credit process.

8. The credit report generation method according to claim 7, after visualizing the at least one node according to the node information respectively corresponding to the at least one node to obtain the credit process and the credit report including the credit process, further comprising: Displaying the credit report through an interactive query interface, and receiving a user's query instruction for a target node in the credit process included in the credit report through the interactive query interface; In response to the query instruction, the credit status change result and the credit risk assessment result corresponding to the target node of the credit process are displayed.

9. The credit report generation method according to claim 1, wherein the categorizing and calculating the plurality of log data to obtain at least one node and the node information corresponding to each of the nodes comprises: According to the preset calculation methods corresponding to different types of log data, the multiple log data are classified and calculated to obtain at least one node and node information corresponding to each of the nodes.

10. A credit report generating device, the device comprising: The log collection module is used to collect multiple log data generated by multiple systems in the process of providing credit services; A log calculation module, used to classify and calculate the plurality of log data to obtain at least one node and node information corresponding to each of the nodes; The report generation module is used to visualize the at least one node according to the node information respectively corresponding to the at least one node, and obtain a credit granting process and a credit granting report including the credit granting process.

11. A computer storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 9.

12. A computer program product, wherein the computer program product stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 9.

13. An electronic device, comprising: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps as claimed in any one of claims 1 to 9.

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