Financial leasing service system and method

Abstract

The invention relates to the technical field of financial leasing service systems, in particular to a financial leasing service system and method, and the system comprises a business input unit which is used for obtaining and inputting the business information of a business initiator; the service approval unit is used for receiving and processing the service information input by the service input unit; the performance supervision unit is used for supervising the examined and approved business in the business examination and approval unit; the query statistics unit is used for querying the processing process of the service approval unit and the supervision record of the performance supervision unit; wherein the performance supervision unit comprises a multi-dimensional adaptive risk assessment and supervision module, the module is used for realizing risk factor identification, risk feature extraction, risk dynamic analysis and comprehensive risk assessment, and a comprehensive and efficient lease service ecological system is constructed by integrating functional modules such as business input, examination and approval, supervision and query statistics.

Description

Technical Field

[0001] The present invention relates to the technical field of financial leasing service systems, and more specifically, to a financial leasing service system and method. Background Art

[0002] With the rapid development of the economy, financial leasing, as an important financing method, plays an increasingly important role in modern business activities. However, along with the expansion of business scale and the increase in complexity, the financial leasing industry is facing increasingly severe risk management challenges. A traditional financial leasing service system mainly relies on static credit scoring models and simple linear risk assessment methods, which have become inadequate in the current rapidly changing market environment.

[0003] Currently, a relatively advanced financial leasing service system in the industry usually adopts a multi-factor scoring card model and combines machine learning algorithms for risk assessment. This method has indeed made certain progress compared with traditional methods and can improve the accuracy of risk identification to a certain extent. However, such systems still have many deficiencies. First of all, they often regard each risk factor as an independent variable and ignore the complex interaction relationships between risk factors. Secondly, most of these systems adopt static risk assessment models and are difficult to adapt to the characteristics of risks changing dynamically over time. Moreover, existing systems perform poorly in dealing with non-linear risk patterns and are often difficult to identify and warn of some hidden and potential risks in a timely manner.

[0004] More critically, there is still much room for improvement in the comprehensiveness and accuracy of risk assessment of existing financial leasing service systems. They usually only focus on credit risk and insufficiently consider other important risk factors such as market risk and operational risk. At the same time, these systems often adopt a simple weighted average method in risk quantification and are difficult to accurately reflect the true impact degree of different risk factors. In addition, existing systems also have deficiencies in risk supervision and warning mechanisms and are difficult to achieve effective monitoring and timely intervention in the entire life cycle of leasing business.

[0005] These problems seriously restrict the healthy development of the financial leasing industry, increase the operating risks of financial institutions, and also affect the quality and efficiency of leasing services. Therefore, developing a service system that can comprehensively, accurately, and dynamically evaluate and manage financial leasing risks has become an urgent need in the industry. Summary of the Invention

[0006] The present invention aims to solve the above technical problems and provides an innovative financial leasing service system and method. By introducing a multi-dimensional adaptive risk assessment and supervision module, the system realizes all-round and dynamic risk management of financial leasing business.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A financial leasing service system, comprising:

[0009] A business entry unit, configured to obtain and enter the business information of the business initiator;

[0010] A business approval unit, configured to receive and process the business information entered by the business entry unit;

[0011] A performance supervision unit, configured to supervise the approved business in the business approval unit; and

[0012] An inquiry and statistics unit, configured to inquire about the processing process of the business approval unit and the supervision records of the performance supervision unit;

[0013] Wherein, the performance supervision unit includes a multi-dimensional adaptive risk assessment and supervision module, which is used to realize risk factor identification, risk feature extraction, risk dynamic analysis and comprehensive risk assessment.

[0014] Preferably, the multi-dimensional adaptive risk assessment and supervision module includes:

[0015] A risk factor matrix construction unit, configured to construct a risk factor matrix;

[0016] A risk eigenvalue decomposition unit, configured to perform eigenvalue decomposition on the risk factor matrix;

[0017] A risk group theory mapping unit, configured to map the eigenvalues to risk groups;

[0018] A risk Bessel function transformation unit, configured to perform Bessel function transformation on the risk group elements; and

[0019] A risk entropy integral assessment unit, configured to calculate the risk entropy integral and obtain the final risk level.

[0020] Preferably, the risk factor matrix construction unit constructs the risk factor matrix through the following steps:

[0021] Obtain risk factor data;

[0022] Calculate the influence degree between risk factors;

[0023] Generate a risk factor matrix R, where R satisfies:

[0024]

[0025] Wherein, R is an n×n risk factor matrix, and r ij Is the influence degree of risk factor i on risk factor j, and n is the total number of risk factors.

[0026] Preferably, the risk eigenvalue decomposition unit performs eigenvalue decomposition through the following steps:

[0027] Based on the risk factor matrix R, calculate its eigenvalues and eigenvectors;

[0028] Construct an eigenvalue diagonal matrix Λ and an eigenvector matrix Q, satisfying:

[0029] R = QΛQ -1 , Λ = diag(λ1, λ2, …, λ n ), where Q is the eigenvector matrix of R, Λ is the eigenvalue diagonal matrix of R, and λ i is the i-th eigenvalue of R, arranged in descending order of absolute value.

[0030] Preferably, the risk group theory mapping unit performs risk group mapping through the following steps: Based on the eigenvalues, construct a risk group G, satisfying:

[0031]

[0032] where G is the risk group, g i is the i-th element in the risk group, θ i is the normalized angle of the i-th risk eigenvalue, e is the base of the natural logarithm, and i is the imaginary unit.

[0033] Preferably, the risk Bessel function transformation unit performs Bessel function transformation through the following steps: Based on the risk group elements, construct a Bessel function and perform transformation, satisfying:

[0034]

[0035] v i = |θ i |

[0036] R i (t) = B i (t) · g i

[0037] where B i (x) is the Bessel function corresponding to the i-th risk factor, is the Bessel function of the first kind, v i is the order of the Bessel function, Γ is the gamma function, and R i (t) is the dynamic function of the i-th risk factor, and t is the time variable.

[0038] Preferably, the risk entropy integral evaluation unit performs risk assessment through the following steps:

[0039] Calculate the risk entropy; perform the risk entropy integral; determine the final risk level, satisfying:

[0040]

[0041] where S(t) is the risk entropy at time t, E is the risk entropy integral, T is the evaluation time period, and Risk Level is the final risk level evaluation, ranging from [0, 10].

[0042] Preferably, the business approval unit includes a first approval process and a second approval process. The first approval process is used to approve the business entered by the business entry unit, and the second approval process is used to approve the business pushed or pulled in the business approval unit.

[0043] Preferably, the performance supervision unit further includes a front-end control sub-unit and a back-end control sub-unit. The front-end control sub-unit is used to implement front-end monitoring on the approved business in the business approval unit, record the performance of the approved business in the business approval unit, and implement automatic collection for the overdue situation of the approved business in the business approval unit or give a risk reminder for the approved business in the business approval unit; the back-end control sub-unit is used to implement back-end monitoring on the approved business in the business approval unit.

[0044] Based on the financial leasing service method of the financial leasing service system, it includes the following steps:

[0045] Obtain and enter the business information of the business initiator through the business entry unit;

[0046] Receive and process the business information through the business approval unit;

[0047] Implement supervision on the approved business through the performance supervision unit, and the supervision process includes:

[0048] Construct a risk factor matrix;

[0049] Perform eigenvalue decomposition on the risk factor matrix;

[0050] Map the eigenvalues to risk groups;

[0051] Perform Bessel function transformation on the risk group elements;

[0052] Calculate the risk entropy integral and obtain the final risk level;

[0053] Query the processing process and supervision records through the query and statistics unit.

[0054] Compared with the prior art, the beneficial effects of the present invention are reflected in the following aspects:

[0055] From a macroscopic perspective, the present invention significantly improves the risk management level of the financial leasing industry. By integrating functional modules such as business entry, approval, supervision, and query statistics, a comprehensive and efficient leasing service ecosystem is constructed. This not only enhances the risk identification and control capabilities of leasing companies but also optimizes the overall business process, laying a solid foundation for the healthy and sustainable development of the industry.

[0056] At the system architecture level, a close collaborative relationship is formed among the various functional modules of the present invention. The business entry unit provides comprehensive and accurate original data for subsequent risk assessment; the business approval unit makes more scientific decisions based on multi-dimensional risk assessment results; the performance supervision unit ensures the persistence and dynamics of risk management; and the query statistics unit provides the necessary data support and feedback mechanism for the entire system. This highly integrated architecture greatly improves the overall efficiency of the system.

[0057] In terms of the core algorithm, the multi-dimensional adaptive risk assessment and supervision module of the present invention is a major highlight. Through a series of innovative algorithms such as risk factor matrix construction, eigenvalue decomposition, group theory mapping, Bessel function transformation, and entropy integral evaluation, multi-dimensional and dynamic analysis of risks is achieved. These algorithms form an organic whole, and the output of each step provides key input for the next step, thus realizing the gradual deepening and optimization of risk assessment.

[0058] It is particularly worth mentioning that the present invention successfully addresses the limitations of the independence assumption of risk factors in traditional methods. By introducing group theory mapping, the system can effectively capture the complex interactions between different risk factors. At the same time, the introduction of Bessel function transformation overcomes the deficiencies of static risk assessment models, enabling the system to accurately depict the time-dynamic characteristics of risks. These two innovations not only theoretically approach the essential characteristics of risks more closely but also significantly improve the accuracy of risk assessment in practice.

[0059] In addition, the risk entropy integral assessment method of the present invention ingeniously introduces the entropy concept in information theory into risk quantification, providing a new perspective for the comprehensive assessment of risks. This method not only considers the absolute magnitudes of various risk factors but also pays attention to the uniformity of risk distribution, thus giving a more comprehensive and profound risk measure.

[0060] In practical applications, the system of the present invention demonstrates significant performance advantages. Compared with traditional methods, it has achieved substantial improvements in key indicators such as the accuracy rate of risk identification and the accuracy rate of default prediction. More importantly, while increasing the return on assets, the system has also reduced the non-performing asset ratio, which means that it has successfully found the optimal balance between risk control and profit maximization.

[0061] Generally speaking, through its innovative system architecture and advanced algorithm design, the present invention not only significantly improves the accuracy and efficiency of financial lease risk management, but also provides a reliable technical path for the digital transformation of the entire industry. Its wide application is expected to greatly reduce the systemic risks of the financial lease industry, improve the efficiency of resource allocation, and thus promote the entire industry to develop towards a more healthy and sustainable direction. Brief Description of the Drawings

[0062] Figure 1 It is the overall process block diagram of the system of the present invention. Detailed Embodiments

[0063] Next, the solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention; obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. For the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] As Figure 1 shown, the present invention discloses a financial lease service system, including a business entry unit 1, a business approval unit 2, a performance supervision unit 3, and a query and statistics unit 4. These units work together to jointly constitute a complete financial lease service process.

[0065] First of all, the business entry unit 1 is responsible for obtaining and entering the business information of the business initiator. In practical applications, this may involve various types of lease businesses, such as equipment leasing, vehicle leasing, or real estate leasing, etc. The business entry unit 1 allows the business initiator to input key information such as lease item description, lease term, rental amount, etc. through a user-friendly interface.

[0066] Next, the business approval unit 2 receives and processes the business information entered by the business entry unit 1. The main responsibility of this unit is to evaluate the feasibility and risks of the lease application. For example, for a large equipment lease application, the business approval unit 2 may consider factors such as the applicant's credit record, financial status, and the market value and depreciation rate of the equipment, etc.

[0067] The performance supervision unit 3 is the core of this system, which supervises the approved operations in the business approval unit 2. What makes this unit unique is that it includes a multi-dimensional adaptive risk assessment and supervision module 31. This module can not only identify risk factors, but also perform risk feature extraction, risk dynamic analysis, and comprehensive risk assessment. This multi-dimensional approach enables the system to evaluate the risks of leasing operations more comprehensively and accurately.

[0068] Finally, the query and statistics unit 4 provides necessary data support for the entire system. It can query the processing process of the business approval unit 2 and the supervision records of the performance supervision unit 3. This not only helps improve the transparency and traceability of the system, but also provides valuable data support for future business decisions.

[0069] Furthermore, as described in claim 2, the multi-dimensional adaptive risk assessment and supervision module 31 includes five key units: a risk factor matrix construction unit 311, a risk eigenvalue decomposition unit 312, a risk group theory mapping unit 313, a risk Bessel function transformation unit 314, and a risk entropy integral assessment unit 315. These five units form a complete risk assessment chain, and the output of each unit provides the necessary input for the next unit.

[0070] Specifically, the risk factor matrix construction unit 311 first constructs a risk factor matrix. In the field of financial leasing, these risk factors may include market risk, credit risk, operational risk, etc. For example, in the leasing operation of a large mechanical equipment, market risk may involve changes in the market demand for the equipment, credit risk may involve the possibility of the lessee's default, and operational risk may involve equipment maintenance and usage issues.

[0071] Next, the risk eigenvalue decomposition unit 312 performs eigenvalue decomposition on the risk factor matrix. This step helps us identify the most important risk factors and understand the relationships between them. For example, we may find that market risk and credit risk are highly correlated in certain types of leasing operations.

[0072] The risk group theory mapping unit 313 then maps the eigenvalues to a risk group. This step transforms the impact of risk factors into a more analyzable mathematical structure. In practical applications, this can help us better understand the interactions between different risk factors.

[0073] The risk Bessel function transformation unit 314 performs Bessel function transformation on the risk group elements. This step introduces the time dimension, enabling us to analyze how risks change over time. For example, we may find that certain types of leasing operations have different risk characteristics at the beginning and end of the lease term.

[0074] Finally, the risk entropy integral evaluation unit 315 calculates the risk entropy integral and obtains the final risk level. This step synthesizes the results of all the previous steps and gives an overall risk assessment. For example, the system may give a risk level from 1 to 10, where 1 represents the lowest risk and 10 represents the highest risk.

[0075] In an embodiment of the present invention, the risk factor matrix construction unit 311 constructs a risk factor matrix through the following steps:

[0076] First, obtain risk factor data. In the field of financial leasing, this may involve collecting various relevant economic indicators, market data, and customer information. For example, for equipment leasing business, we may need to collect information such as the market value change trend of the equipment, the financial status data of the leasing customers, and the overall operating status of the industry.

[0077] Then, calculate the influence degree between risk factors. This step usually requires combining historical data and expert experience. For example, we may find that in some cases, the decline in market demand will significantly increase the default risk of customers.

[0078] Finally, generate a risk factor matrix R. This matrix satisfies the following mathematical expression

[0079]

[0080] where R is an n×n risk factor matrix, and r ij is the influence degree of risk factor i on risk factor j, and n is the total number of risk factors.

[0081] In practical applications, the value of r ij usually ranges from -1 to 1, where a positive value indicates a positive correlation, a negative value indicates a negative correlation, and 0 indicates no correlation. For example, if r 12 = 0.8, this may indicate that risk factor 1 (such as market demand) has a strong positive impact on risk factor 2 (such as credit risk).

[0082] In an embodiment of the present invention, the risk eigenvalue decomposition unit 312 performs eigenvalue decomposition through the following steps: First, based on the risk factor matrix R, calculate its eigenvalues and eigenvectors. This step usually uses numerical calculation methods, such as the power iteration method or the QR algorithm. In the risk assessment of financial leasing, the eigenvalues can be understood as the importance degrees of different risk factors, and the eigenvectors represent the combination ways of these risk factors.

[0083] Then, construct an eigenvalue diagonal matrix Λ and an eigenvector matrix Q, satisfying the following mathematical relationship:

[0084] R = QΛQ -1

[0085] Λ = diag(λ1, λ2, …, λ n )

[0086] where Q is the eigenvector matrix of R, Λ is the diagonal matrix of eigenvalues of R, and λ i is the i-th eigenvalue of R, arranged in descending order by absolute value.

[0087] In practical applications, we usually focus on the largest several eigenvalues because they represent the most important risk factors. For example, if λ1 = 2.5, λ2 = 1.8, λ3 = 0.5, we may focus on the risk factors corresponding to the first two eigenvalues.

[0088] In an embodiment of the present invention, the risk group theory mapping unit 313 performs risk group mapping through the following steps:

[0089] Based on the eigenvalues, construct a risk group G that satisfies the following mathematical relationship:

[0090]

[0091] where G is the risk group, and g i is the i-th element in the risk group, θ i is the normalized angle of the i-th risk eigenvalue, e is the base of the natural logarithm, and i is the imaginary unit.

[0092] This group theory mapping method transforms the influence of risk factors into rotation on the complex plane, enabling us to analyze the interaction between risk factors using group operations. For example, if the sum of the angles of two risk factors is close to an integer multiple of 2π, this may indicate a strong synergistic effect between these two risk factors. In financial leasing business, this may mean that special attention needs to be paid to the combined effect of these two risk factors.

[0093] In this way, a financial leasing service system of the present invention can comprehensively and deeply analyze the risks of leasing business, providing strong support for business decision-making. This risk assessment method based on higher mathematics not only improves the accuracy of risk assessment but also enhances the system's adaptability to complex risk situations.

[0094] In an embodiment of the present invention, the working principle of the risk Bessel function transformation unit 314 is as follows. This unit performs Bessel function transformation through the following steps:

[0095] First, based on the risk group elements, construct a Bessel function and perform transformation. This process satisfies the following mathematical relationship:

[0096]

[0097] v i = |θ i |

[0098] R i (t) = B i (t)·g i

[0099] where B i (x) is the Bessel function corresponding to the i-th risk factor, is the Bessel function of the first kind v i is the order of the Bessel function, Γ is the gamma function, R i (t) is the dynamic function of the i-th risk factor and t is the time variable.

[0100] In financial leasing business, the application of Bessel function transformation can help us better understand the evolution of risks over time. For example, for long-term lease contracts, we may find that some risk factors show periodic changes. Through Bessel function transformation, we can capture this periodicity and thus more accurately predict the future risk situation.

[0101] Preferably, in practical applications, we may choose to use the first few terms of the Bessel function for approximate calculation to improve the calculation efficiency. For example, we may calculate using the first 10 terms, which usually provides sufficient accuracy. The choice of the specific number of terms may need to be weighed according to the actual business requirements and calculation resources.

[0102] In an embodiment of the present invention, the risk entropy integration evaluation unit 315 performs risk assessment through the following steps:

[0103] First, calculate the risk entropy. The calculation formula for the risk entropy is as follows:

[0104]

[0105] where S(t) is the risk entropy at time t, R i (t) is the dynamic function of the i-th risk factor.

[0106] Then, perform the risk entropy integration. The integration formula is as follows:

[0107]

[0108] where E is the risk entropy integration and T is the evaluation time period. Finally, determine the final risk level. The calculation formula for the risk level is as follows:

[0109]

[0110] Among them, Risk Level is the final risk level assessment, with a range of [0, 10].

[0111] In actual financial leasing business, this risk entropy integral assessment method can provide a comprehensive risk indicator. For example, if the calculated Risk Level is 7.5, this may indicate that the leasing business has a relatively high risk and additional risk control measures may be required. Generally, we may divide Risk Level into several intervals. For example, 0 - 3 is low risk, 3 - 6 is medium risk, and 6 - 10 is high risk. This division can help business personnel quickly judge the risk level and take corresponding measures.

[0112] In an embodiment of the present invention, the business approval unit 2 includes a first approval process 21 and a second approval process 22. The first approval process 21 is mainly used to approve the business entered by the business entry unit 1, while the second approval process 22 is used to approve the business pushed or pulled in the business approval unit 2.

[0113] In practical applications, the first approval process 21 may involve a preliminary review of newly submitted lease applications. For example, for an equipment lease application, the first approval process 21 may check the applicant's basic qualifications, the completeness of the application materials, etc. The second approval process 22 may handle some complex cases that require further review, or existing lease businesses that are automatically pushed by the system for re - evaluation.

[0114] This two - layer approval mechanism can improve the approval efficiency and ensure that high - risk or complex lease businesses are subject to more detailed reviews. For example, for a large - amount equipment lease application, even if it passes the first approval process 21, the system may push it to the second approval process 22 for a more in - depth risk assessment.

[0115] In an embodiment of the present invention, the performance supervision unit 3 includes a front - end control sub - unit 32 and a back - end control sub - unit 33.

[0116] The front - end control sub - unit 32 is mainly responsible for implementing front - end monitoring of the approved business. This includes recording the performance of the approved business, automatically collecting overdue payments, and giving risk warnings for the business. In practical applications, the front - end control sub - unit 32 may regularly (e.g., daily) check the payment status of all active lease businesses. If an overdue situation is found, the system may automatically send reminder emails or text messages to the lessee. For cases with a relatively long overdue time (e.g., more than 30 days), the system may trigger a more stringent collection process.

[0117] The backend control subunit 33 is responsible for implementing backend monitoring on the approved operations. This may involve deeper risk monitoring and analysis. For example, the backend control subunit 33 may conduct a comprehensive risk assessment on all leasing operations regularly (such as monthly or quarterly), using the multi-dimensional adaptive risk assessment and supervision module 31 described above. If it is found that the risk level of a certain operation has increased significantly, the system may generate an alert to prompt relevant personnel to intervene.

[0118] In an embodiment of the present invention, a financial leasing service method is also disclosed. This method is based on a financial leasing service system described above and includes the following steps:

[0119] 1. Obtain and input the operation information of the operation initiator through the operation input unit 1. This may involve collecting detailed information of the leasing application through a web interface or a mobile app.

[0120] 2. Receive and process this operation information through the operation approval unit 2. This includes conducting a comprehensive review using the first approval process 21 and the second approval process 22.

[0121] 3. Implement supervision on the approved operations through the performance supervision unit 3. This is the core step of the method and includes the following sub-steps:

[0122] a) Construct a risk factor matrix;

[0123] b) Conduct eigenvalue decomposition on the risk factor matrix;

[0124] c) Map the eigenvalues to risk groups;

[0125] d) Conduct Bessel function transformation on the risk group elements;

[0126] e) Calculate the risk entropy integral and obtain the final risk level;

[0127] 4. Query the processing process and supervision records through the query and statistics unit 4. This step can help operation personnel and management understand the operation status and risk status of the entire leasing operation.

[0128] Through this method, financial leasing service providers can comprehensively and dynamically manage the risks of leasing operations, improving the security and profitability of operations. For example, if the system finds that the risk level of a certain type of equipment leasing is generally high, the company may adjust the pricing strategy or leasing terms of this type of operation to better manage risks.

[0129] Generally speaking, a financial leasing service system and method provided by the present invention greatly improve the risk management ability of leasing business by introducing advanced mathematical tools and advanced risk assessment techniques. This not only helps financial leasing companies better control risks, but also provides more personalized and flexible leasing services for customers.

[0130] To verify the superiority of the present invention, we conducted a series of experiments to compare the embodiments of the present invention with the comparative examples of traditional methods. Below we will introduce the experimental process, results and their analysis in detail.

[0131] Example 1: We selected a medium-sized equipment leasing company as the test object, which mainly engaged in the leasing business of engineering machinery equipment. We used a financial leasing service system of the present invention to conduct risk assessment and management on 1,000 leasing transactions of the company in the past two years. The system adopted the multi-dimensional adaptive risk assessment and supervision module described in detail above, including steps such as risk factor matrix construction, eigenvalue decomposition, group theory mapping, Bessel function transformation and entropy integral evaluation.

[0132] Comparative Example 1: For the same batch of leasing transactions of the same company, we used a traditional credit scoring model for risk assessment and management. This method mainly based on static indicators such as customers' credit records and financial conditions, and calculated risk scores by means of linear weighting.

[0133] We selected the following key indicators to evaluate the performance of the two methods:

[0134] 1. Risk identification accuracy rate: The proportion of correctly identifying high-risk leasing transactions.

[0135] 2. Default prediction accuracy rate: The proportion of correctly predicting the leasing transactions that actually default after maturity.

[0136] 3. Return on assets (ROA): The ratio of net income generated by leased assets to total assets.

[0137] 4. Non-performing asset ratio: The proportion of non-performing leased assets in total leased assets.

[0138] 5. Risk assessment time: The average time required to complete the risk assessment of a leasing transaction.

[0139] The detection methods of these indicators are as follows:

[0140] 1. Risk identification accuracy rate: We defined leasing transactions with a risk level greater than 7 as high-risk transactions. By comparing the high-risk transactions identified by the system with the transactions that actually had problems, the accuracy rate was calculated.

[0141] 2. Default prediction accuracy rate: For lease operations that the system predicts may default, count the proportion of actual defaults.

[0142] 3. Return on assets (ROA): Using the company's financial statement data, calculate the ratio of net income to total assets.

[0143] 4. Non-performing asset ratio: Count the proportion of lease assets overdue for more than 90 days in the total lease assets.

[0144] 5. Risk assessment time: Record the time from the start of assessment to obtaining the risk level for each lease operation, and calculate the average value.

[0145] The following is a comparison table of the experimental results:

[0146]

[0147] It can be seen from the experimental results that a financial lease service system of the present invention is significantly superior to traditional methods in various indicators. Let's analyze these results in depth:

[0148] 1. Risk identification accuracy rate: The system of the present invention has increased by nearly 14 percentage points, which means that we can more accurately identify potential high-risk operations. This improvement is mainly due to our multi-dimensional risk assessment method, which can capture the complex interactions between risk factors that traditional methods may overlook.

[0149] 2. Default prediction accuracy rate: It has increased by 16.6 percentage points, indicating that our system can more accurately predict future default situations. This improvement mainly comes from our dynamic risk assessment method, especially the time dimension analysis introduced by the Bessel function transformation, which enables the system to better capture the evolution of risks over time.

[0150] 3. Return on assets (ROA): It has increased by 1.4 percentage points, which is a significant improvement. A higher ROA means that the company can use its assets more effectively to create profits. This improvement may be due to the fact that our system can more accurately assess risks, enabling the company to expand high-yield but controllable-risk operations with more confidence.

[0151] 4. Non-performing asset ratio: It has decreased by 1.8 percentage points, which is a great progress. A lower non-performing asset ratio means that the company's asset quality is better and the financial risks it faces are smaller. This improvement is mainly due to the high-accuracy risk assessment and real-time monitoring capabilities of our system.

[0152] 5. Risk assessment time: It has been reduced from 2 hours to 15 minutes, and the efficiency has increased by 87.5%. This significant time saving enables the company to respond more quickly to market changes and customer needs, while also reducing operating costs.

[0153] Generally speaking, these results fully demonstrate the superiority of the present invention. Our system not only significantly improves the accuracy and efficiency of risk management, but also helps the company enhance its overall business performance. Notably, while increasing the return on assets, we have also reduced the non-performing asset ratio, indicating that the system has successfully achieved a better balance between risk and return.

[0154] This comprehensive improvement is mainly due to several key innovations of our system: multi-dimensional risk factor analysis, dynamic risk assessment, and risk quantification methods based on advanced mathematics. These innovations enable our system to capture more comprehensively and accurately the complex risk patterns in financial leasing business, thus providing more reliable support for decision-making.

[0155] Based on the above analysis, we can consider that Example 1 represents the best implementation mode of the present invention. It not only performs excellently in various indicators, but also has been fully verified in the actual business environment. This method is particularly suitable for medium and large-sized leasing companies with complex business and variable risks, and can help them gain significant advantages in the highly competitive market.

[0156] The above is only the preferred specific implementation mode of the present invention; however, the protection scope of the present invention is not limited thereto; any person familiar with the field, within the scope disclosed by the present invention, according to the solution of the present invention and its improved conceptions, makes equivalent substitutions or changes, should be covered by the protection scope of the present invention.

Claims

1. A financial leasing service system, characterized in that: include: A business entry unit, used to obtain and enter business information of the business initiator; A business approval unit, used for receiving and processing the business information entered by the business entry unit; A contract performance supervision unit, used to supervise the businesses that have been approved in the business approval unit; as well as A query statistics unit, used to query the processing process of the business approval unit and the supervision record of the performance supervision unit; Among them, the performance supervision unit includes a multi-dimensional adaptive risk assessment and supervision module, which is used to realize risk factor identification, risk feature extraction, risk dynamic analysis and comprehensive risk assessment.

2. A financial leasing service system according to claim 1, characterized in that: The multi-dimensional adaptive risk assessment and supervision module includes: A risk factor matrix construction unit, used to construct a risk factor matrix; A risk eigenvalue decomposition unit, used for performing eigenvalue decomposition on the risk factor matrix; A risk group theory mapping unit, used to map feature values ​​to risk groups; A risk Bessel function transformation unit, used for performing Bessel function transformation on risk group elements; and The risk entropy integral evaluation unit is used to calculate the risk entropy integral and derive the final risk level.

3. A financial leasing service system according to claim 2, characterized in that: The risk factor matrix construction unit constructs the risk factor matrix by the following steps: Obtain risk factor data; Calculate the degree of influence between risk factors; Generate a risk factor matrix R, where R satisfies: Among them, R is the n×n risk factor matrix, r ij is the impact of risk factor i on risk factor j, and n is the total number of risk factors.

4. A financial leasing service system according to claim 3, characterized in that: The risk eigenvalue decomposition unit performs eigenvalue decomposition by the following steps: Based on the risk factor matrix R, calculate its eigenvalues ​​and eigenvectors; Construct the eigenvalue diagonal matrix Λ and the eigenvector matrix Q to satisfy: R=QΛQ -1 , Λ=diag(λ1,λ2,…,λ n ), where Q is the eigenvector matrix of R, Λ is the eigenvalue diagonal matrix of R, and λ i is the i-th eigenvalue of R, arranged in descending order of absolute value.

5. A financial leasing service system according to claim 4, characterized in that: The risk group theory mapping unit performs risk group mapping by the following steps: constructing a risk group G based on the characteristic value, satisfying: Among them, G is the risk group, g i is the i-th element in the risk group, θ i is the normalized angle of the ith risk characteristic value, e is the base of the natural logarithm, and i is the imaginary unit.

6. A financial leasing service system according to claim 5, characterized in that: The risk Bessel function transformation unit performs Bessel function transformation by the following steps: constructing a Bessel function based on the risk group elements and performing transformation to satisfy: v i =|θ i | R i (t)=B i (t)·g i Among them, B i (x) is the Bessel function corresponding to the i-th risk factor, is the first kind Bessel function, ν i is the order of the Bessel function, Γ is the gamma function, R i (t) is the dynamic function of the ith risk factor, and t is the time variable.

7. A financial leasing service system according to claim 2, characterized in that: The risk entropy integral assessment unit performs risk assessment by the following steps: calculating risk entropy; performing risk entropy integral; Determine the final risk level to meet: Where S(t) is the risk entropy at time t, E is the risk entropy integral, T is the evaluation time period, and Risk Level is the final risk level evaluation, ranging from [0,10].

8. A financial leasing service system according to claim 1, characterized in that: The business approval unit includes a first approval process and a second approval process, wherein the first approval process is used to approve the business entered by the business entry unit, and the second approval process is used to approve the business pushed or pulled in the business approval unit.

9. A financial leasing service system according to claim 1, characterized in that: The performance supervision unit also includes a front-end control subunit and a back-end control subunit, wherein the front-end control subunit is used to implement front-end monitoring of the business approved by the business approval unit, record the performance of the business approved by the business approval unit, and automatically collect the overdue business approved by the business approval unit or issue risk warnings for the business approved by the business approval unit; The back-end control subunit is used to implement back-end monitoring on the business that has been approved in the business approval unit.

10. A financial leasing service method based on a financial leasing service system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Acquire and enter the business information of the business initiator through the business entry unit; Receiving and processing the business information through the business approval unit; The approved business is supervised by the performance supervision unit, and the supervision process includes: Construct a risk factor matrix; Performing eigenvalue decomposition on the risk factor matrix; Mapping eigenvalues ​​to risk groups; Perform Bessel function transformation on risk group elements; Calculate the risk entropy score and derive the final risk level; The processing process and supervision records are queried through the query statistics unit.

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