Credit detention-free lease management system and method

Abstract

The invention relates to the technical field of lease management, and discloses a credit detention-free lease management system and method, and the system comprises a data collection module, a data extraction module, a data calculation module, a data analysis module, an execution module, and a feedback module. The problems of data dispersion and low processing efficiency in traditional lease management are solved, accurate credit evaluation and personalized communication strategies improve the payment reminding effect and the user experience, a real-time feedback mechanism ensures optimization of the communication process, the system is more excellent in the aspects of processing overdue risks and improving the operation efficiency, and on the whole, the system has better application prospects. Through accurate comparative analysis and level-to-level management, the system can intervene in time when needed, meanwhile, excessive intervention on users not needing intervention is avoided, the management efficiency is improved, the user service is optimized, and the operation benefits of the leasing business are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of leasing management, and in particular to a credit-free deposit leasing management system and method. Background Art

[0002] Credit-free deposit leasing is a leasing model based on user credit scores, which allows users to waive traditional deposit payments when leasing goods or services. The core of this model is to use the user's credit status to assess their repayment ability and risk level, and then decide whether to allow them to rent without deposit.

[0003] In the traditional leasing model, the high deposit requirement often puts users under financial pressure. At the same time, the deposit management and refund process is complicated and prone to disputes. Although the credit-free deposit leasing management system can replace the deposit with a credit score, it still faces challenges in collecting repayments. The existing system often lacks a refined assessment of the user's credit rating and repayment ability, resulting in inflexible collection methods, which may lead to excessive collection or untimely collection.

[0004] Therefore, we propose a credit-free deposit rental management system to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a credit-free deposit rental management system to solve the problem that the credit-free deposit rental management system proposed in the above background technology can replace the deposit with a credit score, but still faces challenges in collecting repayments. Existing systems often lack a refined assessment of the user's credit rating and repayment ability, resulting in inflexible collection methods, and the possibility of excessive collection or untimely collection.

[0006] To achieve the above object, the present invention provides the following technical solutions: a credit-free deposit rental management system, comprising a data collection module, a data extraction module, a data calculation module, a data analysis module, an execution module and a feedback module;

[0007] The data collection module is used to collect the user's credit data, behavior data, financial data and communication data, and integrate them into multi-source data;

[0008] The data extraction module is used to preprocess the multi-source data collected by the data collection module, and reorganize the preprocessed multi-source data into a first data set, a second data set, a third data set and a fourth data set;

[0009] The data calculation module is used to integrate the first data set, the second data set, the third data set and the fourth data set to calculate the feedback reference coefficient FKC;

[0010] The data analysis module is used to compare the feedback reference coefficient FKC with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current user needs communication intervention according to the first comparison result;

[0011] If the judgment result is that communication intervention is needed, the feedback reference coefficient FKC is integrated with the preset first threshold value Y to generate an intervention level coefficient LJZ, and the intervention level coefficient LJZ is compared with the preset second threshold value R to generate a second comparison result, and according to the second comparison result, the user is divided into the first communication level, the second communication level and the third communication level;

[0012] The execution module executes a corresponding communication process according to the second comparison result. The specific communication process is as follows:

[0013] When the user is at the first communication level, a telephone communication will be conducted on the user's repayment date. If the communication fails and the user does not return the loan on that day, a telephone communication will be conducted between 9:00 and 11:00 the next morning, and an acceptance inspection will be conducted at 12:00 noon. If the loan is still not returned, another telephone communication will be conducted between 2:00 and 5:00 p.m., and an acceptance inspection will be conducted at 6:00 p.m. If the loan is still not returned, the payment will be postponed to 8:00 a.m. on the third day. If the loan is still not returned, the payment will automatically enter the overdue procedure.

[0014] When the user is at the second communication level, a telephone communication will be conducted one day before the user's repayment date, and a telephone communication will be conducted between 9:00 and 11:00 a.m. on the repayment date. An acceptance inspection will be conducted at noon. If the loan has not been returned, a telephone communication will be conducted between 2:00 and 5:00 p.m., and an acceptance inspection will be conducted at 6:00 p.m. If the loan has not been returned, the user will automatically enter the overdue procedure;

[0015] When the user is at the third communication level, telephone communication will be conducted two days before the user's repayment date. If the communication fails and the user does not repay the loan on that day, the user will automatically enter the overdue procedure;

[0016] The feedback module is used to feed back various data and the communication process executed by the search execution module to the terminal.

[0017] Preferably, the data acquisition module includes a first acquisition unit, a second acquisition unit, a third acquisition unit and a fourth acquisition unit;

[0018] The first collection unit is used to collect the user's credit data, the second collection unit is used to collect the user's behavior data, the third collection unit is used to collect the user's financial data, and the fourth collection unit is used to collect the user's communication data;

[0019] The user's credit data, behavior data, financial data and communication data are combined into multi-source data.

[0020] Preferably, the data extraction module is used to preprocess the multi-source data collected by the data acquisition module, dimensionlessly transform the preprocessed multi-source data, and reorganize the dimensionlessly transformed multi-source data, thereby generating a first data set, a second data set, and a third data set.

[0021] Preferably, the first data set includes a credit score value XPF, a credit utilization rate XLL, a credit account age XNX, and a number of overdue payments YQC;

[0022] The second data set includes the number of early settlements TQC, the number of deferred settlements YCC, the average number of monthly account openings KHS, and the total number of accounts ZZC;

[0023] The third data set includes income level SSP, total debt ZWL, disposable income ZPS, and savings rate SHL;

[0024] The fourth data set includes the total number of calls ZTH, the number of answered calls JTC, the number of effective communications YXG, and the communication time GTS.

[0025] Preferably, the data calculation module includes a first calculation unit and a second calculation unit;

[0026] The first calculation unit is used to integrate and calculate the first data set, the second data set, the third data set and the fourth data set into a credit reference coefficient XYC, a behavior reference coefficient XWC, a financial reference coefficient CWC and a communication reference coefficient GTC respectively;

[0027] The second calculation unit is used to integrate the credit reference coefficient XYC, the behavior reference coefficient XWC, the financial reference coefficient CWC and the communication reference coefficient GTC into a feedback reference coefficient FKC.

[0028] Preferably, the first calculation unit calculates and obtains the credit reference coefficient XYC, the behavior reference coefficient XWC, the financial reference coefficient CWC and the communication reference coefficient GTC respectively through the following formulas;

[0029]

[0030]

[0031] Wherein: a1, a2, a3, a4, b1, b2, b3, c1, c2, c3, c4, d1, d2, d3, d4 are weight values, and the values ​​of a1, a2, a3, a4, b1, b2, b3, c1, c2, c3, c4, d1, d2, d3, d4 are adjusted and set by the user;

[0032] XPF is the credit score value, XLL is the credit utilization rate, XNX is the number of years of the credit account, YQC is the number of overdue payments, TQC is the number of pre-settlements, YCC is the number of deferred settlements, KHS is the average monthly number of account openings, ZZC is the total number of accounts, SSP is the income level, ZWL is the total debt, ZPS is the disposable income, SHL is the savings rate, ZTH is the total number of calls, JTC is the number of answered calls, YXG is the number of effective communications, and GTS is the communication time.

[0033] Preferably, the second calculation unit calculates and obtains the feedback reference coefficient FKC by the following formula:

[0034]

[0035] Wherein: e1, e2, e3 and e4 are weight values, and the values ​​of e1, e2, e3 and e4 are adjusted and set by the user, A and B are proportional values, and A+B=1, A≠B≠0;

[0036] XYC is the credit reference coefficient XYC, XWC is the behavioral reference coefficient, CWC is the financial reference coefficient, and GTC is the communication reference coefficient.

[0037] Preferably, the data analysis module includes a first analysis unit and a second analysis unit;

[0038] The first analysis unit is used to compare the calculated feedback reference coefficient FKC with a preset first threshold value Y to generate a first comparison result;

[0039] The second analysis unit is used to integrate and calculate the feedback reference coefficient FKC and the preset first threshold value Y to generate an intervention level coefficient LJZ, and compare the intervention level coefficient LJZ with the preset second threshold value R to generate a second comparison result;

[0040] The first comparison results are as follows:

[0041] When FKC<Y, it means that the current user does not need to communicate and intervene;

[0042] When FKC ≥ Y, it means that the current user does not need to communicate and intervene;

[0043] The second comparison results are as follows:

[0044] When LJZ<R, it means that the current user is at the first communication level;

[0045] When R≤LJZ<R×103%, it means that the current user is at the second communication level;

[0046] When LJZ≥R×103%, it means that the current user is at the third communication level.

[0047] Preferably, the second analysis unit calculates and obtains the intervention magnitude coefficient LJZ by the following formula:

[0048]

[0049] Where: FKC is the feedback reference coefficient, and Y is the first threshold.

[0050] This application also includes a credit-free deposit rental management method, the specific steps are as follows:

[0051] S1. Data collection: Through the data collection module, collect users' credit data, behavior data, financial data and communication data, and integrate them into multi-source data;

[0052] S2, data extraction: using the data extraction module to pre-process the collected multi-source data, clean the noise and outliers in the original data, and then reorganize the processed data into a first data set, a second data set, a third data set and a fourth data set;

[0053] S3, data calculation: In the data calculation module, the first data set, the second data set, the third data set and the fourth data set are integrated and calculated to generate a feedback reference coefficient FKC to comprehensively evaluate the credit status of the user;

[0054] S4, using the data analysis module, comparing the feedback reference coefficient FKC with the preset first threshold value Y to generate a first comparison result, judging whether communication intervention is needed according to the result, and if necessary, calculating the intervention level coefficient LJZ and comparing it with the preset second threshold value R to generate a second comparison result, and dividing the user into the first flux level, the second flux level and the third communication level;

[0055] S5. Execute the corresponding communication process according to the user's communication level through the execution module;

[0056] S6. The feedback module feeds back all data and the communication process executed by the execution module to the terminal, providing a basis for real-time updating and optimization to ensure the effectiveness of the communication process and continuous improvement of the system.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. This system solves the problems of data dispersion and inefficient processing in traditional leasing management through comprehensive data integration and intelligent processing. Accurate credit assessment and personalized communication strategies improve collection results and user experience. The real-time feedback mechanism ensures the optimization of the communication process, making the system perform better in handling overdue risks and improving operational efficiency. Overall, this system improves management efficiency, optimizes user services, and enhances the operational benefits of the leasing business.

[0059] 2. Through precise comparative analysis and hierarchical management, the system can intervene in time when needed, while avoiding excessive intervention on users who do not need intervention. This method not only improves the management efficiency of the system, but also improves the user experience and reduces the negative impact caused by excessive communication. By setting the first threshold Y and the second threshold R and performing dynamic calculations, the system can generate a reasonable intervention strategy based on the feedback reference coefficient FKC, optimize the management effect, and ensure the efficiency and scientificity of the system in credit management and risk control. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a system flow chart of the present invention.

[0061] Figure 2 It is a diagram of the method steps of the present invention.

[0062] In the figure: 1. Data acquisition module; 11. First acquisition unit; 12. Second acquisition unit; 13. Third acquisition unit; 14. Fourth acquisition unit; 2. Data extraction module; 3. Data calculation module; 31. First calculation unit; 32. Second calculation unit; 4. Data analysis module; 41. First analysis unit; 42. Second analysis unit; 5. Execution module; 6. Feedback module. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Example 1: Please refer to Figure 1 , a credit-free deposit rental management system, comprising a data collection module 1, a data extraction module 2, a data calculation module 3, a data analysis module 4, an execution module 5 and a feedback module 6;

[0065] The data collection module 1 is used to collect the user's credit data, behavior data, financial data and communication data, and integrate them into multi-source data;

[0066] The data extraction module 2 is used to preprocess the multi-source data collected by the data collection module 1, and reorganize the preprocessed multi-source data into a first data set, a second data set, a third data set and a fourth data set;

[0067] The data calculation module 3 is used to integrate the first data set, the second data set, the third data set and the fourth data set to calculate the feedback reference coefficient FKC;

[0068] The data analysis module 4 is used to compare the feedback reference coefficient FKC with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current user needs communication intervention according to the first comparison result;

[0069] If the judgment result is that communication intervention is needed, the feedback reference coefficient FKC is integrated with the preset first threshold value Y to generate an intervention level coefficient LJZ, and the intervention level coefficient LJZ is compared with the preset second threshold value R to generate a second comparison result, and according to the second comparison result, the user is divided into the first communication level, the second communication level and the third communication level;

[0070] The execution module 5 executes the corresponding communication process according to the second comparison result. The specific communication process is as follows:

[0071] When the user is at the first communication level, a telephone communication will be conducted on the user's repayment date. If the communication fails and the user does not return the loan on that day, a telephone communication will be conducted between 9:00 and 11:00 the next morning, and an acceptance inspection will be conducted at 12:00 noon. If the loan is still not returned, another telephone communication will be conducted between 2:00 and 5:00 p.m., and an acceptance inspection will be conducted at 6:00 p.m. If the loan is still not returned, the payment will be postponed to 8:00 a.m. on the third day. If the loan is still not returned, the payment will automatically enter the overdue procedure.

[0072] When the user is at the second communication level, a telephone communication will be conducted one day before the user's repayment date, and a telephone communication will be conducted between 9:00 and 11:00 a.m. on the repayment date. An acceptance inspection will be conducted at noon. If the loan has not been returned, a telephone communication will be conducted between 2:00 and 5:00 p.m., and an acceptance inspection will be conducted at 6:00 p.m. If the loan has not been returned, the user will automatically enter the overdue procedure;

[0073] When the user is at the third communication level, telephone communication will be conducted two days before the user's repayment date. If the communication fails and the user does not repay the loan on that day, the user will automatically enter the overdue procedure;

[0074] The feedback module 6 is used to feed back various data and the communication process executed by the search execution module 5 to the terminal.

[0075] In this embodiment: Data collection module 1 is responsible for comprehensively collecting the user's credit data, behavior data, financial data and communication data, and integrating these data into multi-source data. The advantage of this module is that it provides a comprehensive information basis, ensures the integrity and accuracy of the data, and lays a solid foundation for subsequent data processing and analysis. By integrating different types of data, data collection module 1 provides comprehensive and reliable input data for other functions of the system.

[0076] The data extraction module 2 preprocesses the multi-source data acquired by the data acquisition module 1 and classifies the processed data into a first data set, a second data set, a third data set, and a fourth data set. The main task of this module is to improve the quality and consistency of the data and ensure the structuring and usability of the data through data cleaning and organization. This process improves the accuracy of the data and provides a clear structural basis for subsequent data calculation and analysis.

[0077] The data calculation module 3 is responsible for integrating the first data set, the second data set, the third data set and the fourth data set to calculate the feedback reference coefficient FKC. The main advantage of the data calculation module 3 is that it comprehensively evaluates the user's credit status through a scientific calculation formula and provides a quantitative feedback reference coefficient FKC. This indicator provides a basis for the system to comprehensively evaluate the user's credit status, thereby improving the accuracy of the evaluation and the scientific nature of the decision.

[0078] The data analysis module 4 compares the feedback reference coefficient FKC with the preset first threshold value Y to generate a first comparison result, and judges whether communication intervention is needed. If intervention is needed, the module 4 will integrate the feedback reference coefficient FKC with the preset second threshold value R to generate the intervention level coefficient LJZ, and further divide the user into the first flux level, the second flux level and the third communication level. This module improves the pertinence and effectiveness of communication measures through precise data analysis and judgment.

[0079] Execution module 5 executes the corresponding communication process according to the second comparison result. Users with different communication levels will be processed according to different processes. For example, users with the first communication level will go through multiple telephone communications and acceptance, while users with the third communication level will communicate two days before the repayment date. This module improves the collection efficiency through customized communication strategies and ensures the flexibility and adaptability of the communication process.

[0080] Feedback module 6 is responsible for feeding back various data and the communication process executed by execution module 5 to the terminal. The advantage of this module is that it provides real-time feedback to ensure that all communication actions and results are recorded and communicated. The feedback mechanism enables the system to continuously optimize communication processes and strategies, improve the overall performance of the system and user satisfaction, and provide the necessary basis for system improvement and adjustment.

[0081] This system solves the problems of data dispersion and inefficient processing in traditional leasing management through comprehensive data integration and intelligent processing. Accurate credit assessment and personalized communication strategies improve collection results and user experience. The real-time feedback mechanism ensures the optimization of the communication process, making the system perform better in handling overdue risks and improving operational efficiency. Overall, this system improves management efficiency, optimizes user services, and enhances the operational benefits of the leasing business.

[0082] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13 and a fourth acquisition unit 14;

[0083] The first collection unit 11 is used to collect the user's credit data, the second collection unit 12 is used to collect the user's behavior data, the third collection unit 13 is used to collect the user's financial data, and the fourth collection unit 14 is used to collect the user's communication data;

[0084] Users' credit data, behavioral data, financial data, and communication data become multi-source data.

[0085] In this embodiment: the first collection unit 11 is responsible for collecting the user's credit data, the second collection unit 12 is responsible for collecting behavioral data, the third collection unit 13 is responsible for collecting financial data, and the fourth collection unit 14 is responsible for collecting communication data. This comprehensive data coverage ensures that the system can obtain the most comprehensive information when evaluating the user's credit, reducing information blind spots.

[0086] By integrating the user's credit data, behavioral data, financial data, and communication data into multi-source data, the data collection module 1 provides a comprehensive user data view. This integration improves the accuracy and consistency of the data, laying a solid foundation for subsequent data analysis and calculation. Each collection unit focuses on different types of data collection, ensuring the quality and integrity of each type of data, avoiding missing or inaccurate information due to a single data source, and thus improving the reliability of the overall data.

[0087] By comprehensively collecting a variety of data, the system can comprehensively evaluate the user's credit status, thereby supporting more accurate decision-making. By dividing the data into multiple units for collection, not only the efficiency and accuracy of data collection are improved, but also the time and cost of data collection are reduced, and the overall efficiency of the system is improved. In addition, detailed data collection enables the system to provide more personalized services and communication strategies, thereby improving the user experience. This division of labor and cooperation method enhances the scientificity and effectiveness of decision-making and provides the system with more comprehensive user information support.

[0088] Example 3: Please refer to Figure 1 The data extraction module 2 is used to preprocess the multi-source data collected by the data collection module 1, and to dimensionlessly transform the preprocessed multi-source data, and to reorganize the dimensionlessly transformed multi-source data, thereby generating a first data set, a second data set, and a third data set.

[0089] The first data set includes the credit score value XPF, the credit utilization rate XLL, the credit account age XNX and the number of overdue payments YQC;

[0090] The second data set includes the number of early settlements TQC, the number of deferred settlements YCC, the average number of monthly account openings KHS, and the total number of accounts ZZC;

[0091] The third data set includes income level SSP, total debt ZWL, disposable income ZPS, and savings rate SHL;

[0092] The fourth data set includes the total number of calls ZTH, the number of answered calls JTC, the number of effective communications YXG, and the communication time GTS.

[0093] In this embodiment: Data extraction module 2 pre-processes the raw data to clean the noise and outliers in the data, thereby improving the quality of the data. This step ensures that subsequent data analysis and calculations are based on high-quality data, thereby improving the accuracy and reliability of the system as a whole. In addition, data extraction module 2 performs dimensionless processing on multi-source data, eliminating the dimensional differences between different data indicators, so that various types of data can be compared and analyzed under unified standards. This processing method enhances the consistency of the data, avoids data distortion caused by different dimensions, and thus improves the accuracy of data calculation.

[0094] After dimensionless processing, the data extraction module 2 reorganizes the data into the first data set, the second data set, and the third data set. This classification makes the data more structured and convenient for subsequent processing and analysis. The generated first data set, second data set, and third data set respectively cover indicators of different aspects such as credit, behavior, finance, and communication, providing detailed data support for comprehensive analysis. Accurate classification and organization provide a rich analytical basis for the system, ensuring that the system can conduct comprehensive and accurate credit assessment.

[0095] Through structured data organization, the data extraction module 2 improves the efficiency of data processing. The classified data sets enable subsequent data calculation and analysis to be carried out more quickly, reducing processing time and improving the overall operating efficiency of the system. Each data set covers multi-dimensional information on user credit, behavior, finance and communication. This multi-dimensional data integration and analysis supports the system to conduct a comprehensive user credit assessment, thereby helping the system to formulate effective strategies and decisions.

[0096] Example 4: Please refer to Figure 1 , the data calculation module 3 includes a first calculation unit 31 and a second calculation unit 32;

[0097] The first calculation unit 31 is used to integrate the first data set, the second data set, the third data set and the fourth data set into a credit reference coefficient XYC, a behavior reference coefficient XWC, a financial reference coefficient CWC and a communication reference coefficient GTC respectively;

[0098] The second calculation unit 32 is used for integrating the credit reference coefficient XYC, the behavior reference coefficient XWC, the financial reference coefficient CWC and the communication reference coefficient GTC to calculate the feedback reference coefficient FKC.

[0099] In this embodiment: the first calculation unit 31 integrates different data sets into credit reference coefficients XYC, behavioral reference coefficients XWC, financial reference coefficients CWC, and communication reference coefficients GTC. This process ensures that the system can comprehensively evaluate the user's credit status from multiple perspectives and provide comprehensive evaluation indicators for subsequent decision-making. By integrating various data sets, the reference coefficients generated by the first calculation unit 31 provide detailed quantitative data, making the evaluation of the user's credit status more scientific and accurate.

[0100] The second calculation unit 32 integrates multiple reference coefficients into a feedback reference coefficient FKC to provide a comprehensive evaluation result. This comprehensive evaluation capability comprehensively integrates the user's credit, behavior, finance and communication status to form a unified evaluation standard. By integrating various reference coefficients into the feedback reference coefficient FKC, the system can perform more in-depth data analysis, thereby improving the understanding and prediction capabilities of the user's credit status and supporting more accurate decision-making and strategy formulation.

[0101] The structured calculation process of the data calculation module 3 improves the efficiency of data processing. Through step-by-step calculation and integration, the data calculation module 3 can quickly generate the required reference coefficients, reduce data processing time, and improve the overall efficiency of the system. The generated feedback reference coefficient FKC provides a comprehensive credit assessment tool for the system, enabling the system to formulate more effective credit management strategies and measures, thereby optimizing the accuracy and operational efficiency of credit management.

[0102] Example 5: Please refer to Figure 1 , the first calculation unit 31 calculates and obtains the credit reference coefficient XYC, the behavior reference coefficient XWC, the financial reference coefficient CWC and the communication reference coefficient GTC respectively through the following formulas;

[0103]

[0104] Wherein: a1, a2, a3, a4, b1, b2, b3, c1, c2, c3, c4, d1, d2, d3, d4 are weight values, and the values ​​of a1, a2, a3, a4, b1, b2, b3, c1, c2, c3, c4, d1, d2, d3, d4 are adjusted and set by the user;

[0105] XPF is the credit score value, XLL is the credit utilization rate, XNX is the number of years of the credit account, YQC is the number of overdue payments, TQC is the number of pre-settlements, YCC is the number of deferred settlements, KHS is the average monthly number of account openings, ZZC is the total number of accounts, SSP is the income level, ZWL is the total debt, ZPS is the disposable income, SHL is the savings rate, ZTH is the total number of calls, JTC is the number of answered calls, YXG is the number of effective communications, and GTS is the communication time.

[0106] The second calculation unit 32 calculates and obtains the feedback reference coefficient FKC by the following formula:

[0107]

[0108] Wherein: e1, e2, e3 and e4 are weight values, and the values ​​of e1, e2, e3 and e4 are adjusted and set by the user, A and B are proportional values, and A+B=1, A≠B≠0;

[0109] XYC is the credit reference coefficient XYC, XWC is the behavioral reference coefficient, CWC is the financial reference coefficient, and GTC is the communication reference coefficient.

[0110] In this embodiment, by comprehensively weighting and processing multiple parameters, accurate calculation of each reference coefficient is achieved. This accurate calculation helps the system to more accurately assess the credit status and risk level of users. Each calculation formula fully considers various factors, so that potential high-risk users can be carefully identified and corresponding management measures can be taken to reduce the overall risk.

[0111] The weight value is adjusted by the user according to actual needs and business goals, giving the system the ability to dynamically optimize the impact of different reference coefficients. This flexibility enables the system to adjust the calculation of the feedback reference coefficient FKC according to specific circumstances, enhancing the system's adaptability and personalization capabilities.

[0112] By combining different parameters and weights, the calculation results can accurately reflect the different impacts of various factors on the user's credit status. The system can flexibly adjust the relative importance of these factors, thereby improving the flexibility and accuracy of the calculation.

[0113] The comprehensive credit reference coefficient XYC, behavioral reference coefficient XWC, financial reference coefficient CWC and communication reference coefficient GTC generate the feedback reference coefficient FKC. This comprehensive evaluation capability integrates all aspects of credit information, provides a comprehensive user credit evaluation, and improves the accuracy and comprehensiveness of the overall evaluation. The user setting of the weight value and the adjustment of the proportion value enable the system to optimize the impact of different coefficients in the calculation of the feedback reference coefficient FKC to ensure the rationality of the comprehensive evaluation.

[0114] The multi-coefficient combination calculation in the formula can carefully reflect the user's overall credit status and potential risks. Through this comprehensive feedback, the system effectively identifies high-risk users and takes corresponding management measures. Taking into account the impact of different reference coefficients, the feedback reference coefficient FKC provides a unified evaluation standard for the decision-making process, optimizes decision support, and improves the scientificity and accuracy of decision-making, thereby achieving better performance in credit management and risk control.

[0115] Example 6: Please refer to Figure 1 , the data analysis module 4 includes a first analysis unit 41 and a second analysis unit 42;

[0116] The first analysis unit 41 is used to compare the calculated feedback reference coefficient FKC with a preset first threshold value Y to generate a first comparison result;

[0117] The second analysis unit 42 is used to integrate and calculate the feedback reference coefficient FKC and the preset first threshold value Y to generate an intervention level coefficient LJZ, and compare the intervention level coefficient LJZ with the preset second threshold value R to generate a second comparison result;

[0118] The first comparison results are as follows:

[0119] When FKC<Y, it means that the current user does not need to communicate and intervene;

[0120] When FKC ≥ Y, it means that the current user does not need to communicate and intervene;

[0121] The second comparison results are as follows:

[0122] When LJZ<R, it means that the current user is at the first communication level;

[0123] When R≤LJZ<R×103%, it means that the current user is at the second communication level;

[0124] When LJZ≥R×103%, it means that the current user is at the third communication level.

[0125] The second analysis unit 42 calculates and obtains the intervention magnitude coefficient LJZ by the following formula:

[0126]

[0127] Where: FKC is the feedback reference coefficient, and Y is the first threshold.

[0128] In this embodiment: the first analysis unit 41 generates a first comparison result by comparing the feedback reference coefficient FKC with a preset first threshold value Y. This method ensures that the system can accurately determine whether communication intervention with the user is required, thereby avoiding unnecessary intervention and improving the accuracy of communication decisions.

[0129] By calculating the intervention level coefficient LJZ, the system can dynamically adjust the communication level standard to ensure that the communication strategy can be optimized according to the actual situation. This dynamic adjustment capability enables the system to better adapt to the needs of different users and improves the flexibility and adaptability of communication management.

[0130] The second analysis unit 42 divides the users into different communication levels by calculating the intervention level coefficient LJZ and comparing it with the second threshold R. This levelization strategy enables the system to take corresponding communication measures according to the specific situation of the user, realize more detailed communication management, and improve the effectiveness of communication. Specifically, the second analysis unit 42 divides the users into the first flux level, the second flux level, and the third communication level, and adopts corresponding communication strategies according to different levels. This hierarchical management ensures the rational allocation of resources and optimizes the communication efficiency.

[0131] Through precise comparative analysis and hierarchical management, the system can intervene in time when needed, while avoiding excessive intervention on users who do not need intervention. This method not only improves the management efficiency of the system, but also improves the user experience and reduces the negative impact of excessive communication. By setting the first threshold Y and the second threshold R and performing dynamic calculations, the system can generate a reasonable intervention strategy based on the feedback reference coefficient FKC, optimize the management effect, and ensure the efficiency and scientificity of the system in credit management and risk control.

[0132] See also Figure 2 ,This application also includes a credit-free deposit leasing management method, the specific steps are as follows;

[0133] S1. Data collection: Through the data collection module 1, collect the user's credit data, behavior data, financial data and communication data, and integrate them into multi-source data;

[0134] S2, data extraction: using the data extraction module 2 to pre-process the collected multi-source data, clean the noise and outliers in the original data, and then reorganize the processed data into a first data set, a second data set, a third data set and a fourth data set;

[0135] S3, data calculation: In the data calculation module 3, the first data set, the second data set, the third data set and the fourth data set are integrated and calculated to generate a feedback reference coefficient FKC to comprehensively evaluate the credit status of the user;

[0136] S4, using the data analysis module 4, the feedback reference coefficient FKC is compared with the preset first threshold value Y to generate a first comparison result, and whether communication intervention is needed is determined according to the result. If necessary, the intervention level coefficient LJZ is calculated and compared with the preset second threshold value R to generate a second comparison result, and the user is divided into the first flux level, the second flux level and the third communication level;

[0137] S5, executing module 5, executing a corresponding communication process according to the user's communication level;

[0138] S6. Feedback module 6 feeds back all data and the communication process executed by execution module 5 to the terminal, providing a basis for real-time updating and optimization, ensuring the effectiveness of the communication process and continuous improvement of the system.

[0139] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0140] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A credit-free deposit rental management system, characterized by: It includes a data acquisition module (1), a data extraction module (2), a data calculation module (3), a data analysis module (4), an execution module (5) and a feedback module (6); The data collection module (1) is used to collect the user's credit data, behavior data, financial data and communication data, and integrate them into multi-source data; The data extraction module (2) is used to pre-process the multi-source data collected by the data collection module (1), and reorganize the pre-processed multi-source data into a first data set, a second data set, a third data set and a fourth data set; The data calculation module (3) is used to integrate the first data set, the second data set, the third data set and the fourth data set to calculate the feedback reference coefficient FKC; The data analysis module (4) is used to compare the feedback reference coefficient FKC with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current user needs communication intervention according to the first comparison result; If the judgment result is that communication intervention is needed, the feedback reference coefficient FKC is integrated with the preset first threshold value Y to generate an intervention level coefficient LJZ, and the intervention level coefficient LJZ is compared with the preset second threshold value R to generate a second comparison result, and according to the second comparison result, the user is divided into the first communication level, the second communication level and the third communication level; The execution module (5) executes a corresponding communication process according to the second comparison result. The specific communication process is as follows: When the user is at the first communication level, a telephone communication will be conducted on the user's repayment date. If the communication fails and the user does not return the loan on that day, a telephone communication will be conducted between 9:00 and 11:00 the next morning, and an acceptance inspection will be conducted at 12:00 noon. If the loan is still not returned, another telephone communication will be conducted between 2:00 and 5:00 p.m., and an acceptance inspection will be conducted at 6:00 p.m. If the loan is still not returned, the payment will be postponed to 8:00 a.m. on the third day. If the loan is still not returned, the payment will automatically enter the overdue procedure. When the user is at the second communication level, a telephone communication will be conducted one day before the user's repayment date, and a telephone communication will be conducted between 9:00 and 11:00 a.m. on the repayment date. An acceptance inspection will be conducted at noon. If the loan has not been returned, a telephone communication will be conducted between 2:00 and 5:00 p.m., and an acceptance inspection will be conducted at 6:00 p.m. If the loan has not been returned, the user will automatically enter the overdue procedure; When the user is at the third communication level, telephone communication will be conducted two days before the user's repayment date. If the communication fails and the user does not repay the loan on that day, the user will automatically enter the overdue procedure; The feedback module (6) is used to feed back various data and the communication process executed by the search execution module (5) to the terminal.

2. A credit-free deposit rental management system according to claim 1, characterized in that: The data acquisition module (1) comprises a first acquisition unit (11), a second acquisition unit (12), a third acquisition unit (13) and a fourth acquisition unit (14); The first collection unit (11) is used to collect credit data of the user, the second collection unit (12) is used to collect behavior data of the user, the third collection unit (13) is used to collect financial data of the user, and the fourth collection unit (14) is used to collect communication data of the user; The user's credit data, behavior data, financial data and communication data are combined into multi-source data.

3. A credit-free deposit rental management system according to claim 2, characterized in that: The data extraction module (2) is used to preprocess the multi-source data collected by the data collection module (1), and to dimensionlessly transform the preprocessed multi-source data, and to reorganize the dimensionlessly transformed multi-source data, thereby generating a first data set, a second data set, and a third data set.

4. A credit-free deposit rental management system according to claim 3, characterized in that: The first data set includes a credit score value XPF, a credit utilization rate XLL, a credit account age XNX, and a number of overdue payments YQC; The second data set includes the number of early settlements TQC, the number of deferred settlements YCC, the average number of monthly account openings KHS, and the total number of accounts ZZC; The third data set includes income level SSP, total debt ZWL, disposable income ZPS, and savings rate SHL; The fourth data set includes the total number of calls ZTH, the number of answered calls JTC, the number of effective communications YXG, and the communication time GTS.

5. A credit-based rental management system according to claim 4, characterized in that: The data calculation module (3) comprises a first calculation unit (31) and a second calculation unit (32); The first calculation unit (31) is used to integrate the first data set, the second data set, the third data set and the fourth data set into a credit reference coefficient XYC, a behavior reference coefficient XWC, a financial reference coefficient CWC and a communication reference coefficient GTC respectively; The second calculation unit (32) is used to integrate the credit reference coefficient XYC, the behavior reference coefficient XWC, the financial reference coefficient CWC and the communication reference coefficient GTC to calculate the feedback reference coefficient FKC.

6. A credit-based rental management system according to claim 5, characterized in that: The first calculation unit (31) calculates and obtains the credit reference coefficient XYC, the behavior reference coefficient XWC, the financial reference coefficient CWC and the communication reference coefficient GTC respectively through the following formulas; Wherein: a1, a2, a3, a4, b1, b2, b3, c1, c2, c3, c4, d1, d2, d3, d4 are weight values, and the values ​​of a1, a2, a3, a4, b1, b2, b3, c1, c2, c3, c4, d1, d2, d3, d4 are adjusted and set by the user; XPF is the credit score value, XLL is the credit utilization rate, XNX is the number of years of the credit account, YQC is the number of overdue payments, TQC is the number of pre-settlements, YCC is the number of deferred settlements, KHS is the average monthly number of account openings, ZZC is the total number of accounts, SSP is the income level, ZWL is the total debt, ZPS is the disposable income, SHL is the savings rate, ZTH is the total number of calls, JTC is the number of answered calls, YXG is the number of effective communications, and GTS is the communication time.

7. A credit-based rental management system according to claim 6, characterized in that: The second calculation unit (32) calculates and obtains the feedback reference coefficient FKC by the following formula; Where: e 1, e2, e3 and e4 are weight values, and the values ​​of e1, e2, e3 and e4 are adjusted and set by the user, A and B are proportional values, and A+B=1, A≠B≠0; XYC is the credit reference coefficient XYC, XWC is the behavioral reference coefficient, CWC is the financial reference coefficient, and GTC is the communication reference coefficient.

8. A credit-based rental management system according to claim 7, characterized in that: The data analysis module (4) comprises a first analysis unit (41) and a second analysis unit (42); The first analysis unit (41) is used to compare the calculated feedback reference coefficient FKC with a preset first threshold value Y to generate a first comparison result; The second analysis unit (42) is configured to integrate and calculate the feedback reference coefficient FKC with a preset first threshold Y to generate an intervention magnitude coefficient LJZ, and compare the intervention magnitude coefficient LJZ with a preset second threshold R to generate a second comparison result; The specific content of the first comparison result is as follows; When FKC < Y, it means that the current user does not need communication intervention; When FKC ≥ Y, it means that the current user does not need communication intervention; The specific content of the second comparison result is as follows; When LJZ < R, it means that the current user is at the first communication level; When R ≤ LJZ < R × 103%, it means that the current user is at the second communication level; When LJZ ≥ R × 103%, it means that the current user is at the third communication level.

9. A credit-based rental management system according to claim 8, characterized in that: The second analysis unit (42) obtains the intervention magnitude coefficient LJZ through the following formula calculation; In the formula: FKC is the feedback reference coefficient, and Y is the first threshold.

10. A credit-free deposit leasing management method, characterized by: This method is used to execute a credit-free deposit rental management system described in any one of claims 1 to 9, and the specific steps are as follows; S1. Data collection: Through the data collection module (1), collect the user's credit data, behavior data, financial data, and communication data, and integrate them into multi-source data; S2. Data extraction: Use the data extraction module (2) to preprocess the collected multi-source data, clean the noise and outliers in the original data, and then reorganize the processed data into a first data set, a second data set, a third data set, and a fourth data set; S3. Data calculation: In the data calculation module (3), integrate and calculate the first data set, the second data set, the third data set, and the fourth data set to generate a feedback reference coefficient FKC to comprehensively evaluate the user's credit status; S4. Use the data analysis module (4) to compare the feedback reference coefficient FKC with a preset first threshold Y to generate a first comparison result, and judge whether communication intervention is needed according to this result. If so, calculate the intervention magnitude coefficient LJZ, and compare it with a preset second threshold R to generate a second comparison result, and divide the user into a first throughput level, a second throughput level, and a third communication level; S5. Through the execution module (5), execute the corresponding communication process according to the user's communication level; S6. The feedback module (6) feeds back all data and the communication process executed by the execution module (5) to the terminal to provide a basis for real-time update and optimization, and ensure the effectiveness of the communication process and the continuous improvement of the system.

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