Dynamic evaluation system for leasing risk

Abstract

The invention discloses a lease risk dynamic evaluation system and method, relates to the technical field of lease, and solves the technical problem that the prior art only aims at the negative influence of a lease user on a lease provider, guarantees the benefits of the lease provider, but ignores the benefits of the lease user. According to the invention, based on operation information, the net profit, the bad record score, the quality index and the after-sales improvement proportion of the lessee are analyzed; further analyzing a financial risk level, a bad record risk level, a quality risk level and an after-sales risk level; integrating the risk levels of the operation information to analyze the operation risk level of the lessee; the overall operation condition of the renter is reflected; according to the technical scheme of the invention, in the market environment where a plurality of leasing providers of the same type exist, the leasing user can carry out detailed comparison and evaluation according to the risk condition of each leasing provider so as to make a choice, so that the selection of leasing service providers with relatively high potential risks or poor reputation is effectively avoided, and the benefits of the leasing user are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of leasing, and in particular relates to a dynamic leasing risk assessment system. Background Art

[0002] With the popularization of the concept of sharing economy, shared bicycles, shared power banks, shared cars, etc. have gradually come into people's view; the rental industry has become a mainstream industry with potential for development.

[0003] Current research on the leasing industry mainly focuses on credit risk assessment of leasing customers, aiming to accurately determine the credit status of leasing customers to help leasing businesses make decisions on whether to approve leasing applications. For example, invention patent applications with publication numbers CN117710110A and CN111292181A both aim to address the problem that leasing risk assessment methods in the existing technology are difficult to meet the assessment accuracy requirements of the leasing industry; they only target the negative impact of leasing users on lessors, protect the interests of lessors, and ignore the interests of leasing users. Therefore, the present invention provides a dynamic leasing risk assessment system. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a dynamic rental risk assessment system to solve the technical problem that the prior art only focuses on the negative impact of leasing users on lessors, protects the interests of lessors, but ignores the interests of leasing users.

[0005] To achieve the above-mentioned purpose, a first aspect of the present invention is a dynamic assessment system for leasing risks, comprising a data acquisition module and a risk assessment module;

[0006] Data collection module: used to collect the lessor's operational information; operational information includes financial information, bad record information, leased product information and after-sales information;

[0007] Risk Assessment Module: used to analyze the lessor's operational risk level; the operational risk level is obtained based on the risk level analysis of various operational information; the risk levels of various operational information are financial risk level, bad record risk level, quality risk level, and after-sales risk level; and,

[0008] Analyze the net profit of the lessor during the operating cycle and determine whether the net profit can support the operation of the next operating cycle; if not, analyze the financial risk level; if yes, do nothing;

[0009] Analyze the bad record score of the lessor based on the bad information; analyze the bad record risk level based on the bad record score;

[0010] Extract the leased product's usage period and analyze the leased product's damage index based on the leased product information; analyze the leased product's quality index based on the usage period and damage index; and analyze the quality risk level based on the quality index;

[0011] Analyze the after-sales perfection rate of the lessor based on after-sales information; and analyze the after-sales risk level.

[0012] Preferably, analyzing the net profit of the lessor during the operation cycle and determining whether the net profit can support the operation of the next operation cycle includes:

[0013] Extract financial information for the operating cycle; where financial information includes revenue data and expenditure data;

[0014] Net profit QJL is calculated using the formula QJL = SR - ZC; where SR is revenue and ZC is expenditure;

[0015] Extract the lessor's expenditure data for several days from historical data, average the expenditure data to get the average daily expenditure, and multiply the average daily expenditure by the number of days in the operating cycle to get the expenditure required for the operating cycle; where the operating cycle is measured in days as the smallest unit;

[0016] If the net profit is greater than the expenditure of the operating cycle, it can support the operation of the next operating cycle; otherwise, it cannot support the operation of the next operating cycle.

[0017] The present invention avoids errors caused by estimation or rough estimation by directly calculating the net profit using income data and expenditure data, making the financial analysis results more accurate and reliable. It also analyzes the approximate daily expenditure of the lessor through historical data, and then estimates the expenditure required during the operating cycle, and evaluates in advance whether the net profit of the current operating cycle is sufficient to support the operation of the next cycle. This helps companies to adjust their business strategies in a timely manner, optimize resource allocation, avoid risks such as broken capital chains, and lay the foundation for subsequent analysis of financial risk conditions.

[0018] Preferably, the analyzing the bad record score of the lessor based on the bad information includes:

[0019] Extract the leasing codes of conduct from the Leasing Business Association and rate each code of conduct according to its importance;

[0020] The bad record score BLF of the lessor during the operation cycle is calculated by the formula BLF=Σ(FSi×Ni); where i is the code of conduct number, i=0, 1,…, M, M is a positive integer; FSi is the score of the violated code of conduct i; Ni is the number of violations of code of conduct i during the operation cycle; where experts score the importance of the code of conduct; and Σ is the sum of i.

[0021] The present invention introduces experts to score the importance of behavioral norms, so that the weights of different behavioral norms in the evaluation system can be reasonably determined. This scoring method based on expert experience increases the scientific nature of the evaluation results; by calculating the bad record scores within the operating cycle, a dynamic evaluation of the lessor's behavior is achieved, which timely reflects the lessor's operating conditions and behavioral performance, provides a strong regulatory basis for regulatory authorities, and also provides lessors with opportunities for self-examination and continuous improvement, and lays the foundation for subsequent analysis of financial risk conditions.

[0022] Preferably, based on the rental product information, analyzing the damage index of the rental product includes:

[0023] Extract the fault problems and repair times of the leased products from the historical maintenance records; the experts set the impact coefficient of each fault problem on the leased product, marked as A t , t is the fault problem type;

[0024] By the formula SHD=Σ x Σ t (A xt ×C xt ) calculates the damage index SHD of the rental product; where x is the rental product number; C xt is the number of maintenance times, Σ t Sum over t, Σ x Sums x.

[0025] The present invention analyzes the damage index of leased goods by comprehensively considering the historical failure problems of all leased goods and the number of repairs for the failure problems, as well as using the influence coefficient set by experts. The damage index comprehensively reflects the overall damage status of the lessor's goods. At the same time, the lessor can clearly see its own problems and shortcomings in product maintenance and management, which helps the lessor to identify improvement points, take targeted measures, continuously improve product quality and service levels, enhance market competitiveness, and lay the foundation for subsequent analysis of the quality status of leased goods.

[0026] Preferably, the analyzing the quality index of the leased goods includes:

[0027] Extract the current usage period of the rental product from the rental product information and perform subtraction processing on the rated usage period to obtain the usable period, and count the number of rental products whose usable period is less than the period threshold;

[0028] By the formula ZLX=e SHD +e QT The quality evaluation index ZLX is calculated, where QT is the number of rental products whose usable period is less than the period threshold.

[0029] This method extracts the current usage period and rated usage period of a leased product in real time and calculates the difference to determine the usable period. This ensures real-time and accurate assessment. It allows lessors and regulators to promptly understand the remaining useful life of leased products, providing reliable data support for decision-making. Furthermore, by counting the number of leased products with a usable period less than a threshold and assessing the quality of leased products based on a damage index, lessors can promptly identify any issues or deficiencies in their product maintenance and management. This helps lessors continuously optimize product management processes, improve product quality, and enhance market competitiveness. It also lays the foundation for subsequent analysis of the quality risk status of leased products.

[0030] Preferably, the analysis of the lessor's after-sales perfection ratio based on after-sales information includes:

[0031] The number of after-sales service items of the lessor is counted and compared with the number of standard after-sales service items to obtain the after-sales perfection ratio.

[0032] Preferably, analyzing the risk level of each item of operational information includes:

[0033] Set K preset thresholds, marked as A r,j ; Where r is the type of operation information; j is the preset threshold number, j = 0, 1, ..., K, K is a positive integer; A r,j Increase successively;

[0034] Compare the operation value of each operation information with the corresponding preset threshold in turn; when the operation value is greater than A r,j and less than A r,j+1 When the operation value is less than A, the risk level of the operation information is Q, and Q = j; r,0 When , the risk level of the operation information is 0; the operation value of the operation information includes the absolute difference in profit, the quality index of bad record score and the after-sales improvement ratio; the absolute value of the profit difference is the absolute difference between the net profit and the profit threshold.

[0035] The present invention sets multiple preset thresholds that increase in sequence, and this method can achieve a refined assessment of the risk level of operational information. Different threshold intervals correspond to different risk levels, making the assessment results more detailed and accurate, helping lessors or managers to understand the operational status more accurately. At the same time, the number and specific values ​​of the preset thresholds can be adjusted according to actual conditions to adapt to different operating environments and business needs. This flexibility enables the method to be widely used in various leasing business scenarios to meet the personalized needs of different lessors for risk assessment; and by comparing the operational value with the preset threshold to determine the risk level, the method is simple and intuitive, easy to understand and implement. Lessors or managers can quickly assess the risk level of operational information and take corresponding management measures without complex calculations or professional knowledge.

[0036] It should be noted that the larger the risk level value, the higher the risk level of the corresponding operational information.

[0037] Preferably, the analysis of the lessor's operational risk level includes:

[0038] By the formula YFD=Σ(H r ×A r,j ) calculates the lessor's operational risk level YFD; where H r is the weight coefficient corresponding to the operation information, H r >0 and ΣH r =1;Σsum r.

[0039] The present invention analyzes the risk level of the lessor itself through the risk level of information and the weight coefficient corresponding to each operational information, comprehensively reflecting the overall operational risk status of the lessor and avoiding the one-sidedness of single indicator evaluation; when there are several lessors of the same type, the lessee can make a choice based on the risk status of the lessor, providing a basis for judgment for the lessee and avoiding choosing those leasing service providers with higher potential risks or poor reputation.

[0040] Preferably, the evaluation method of the lease risk dynamic evaluation system includes the following steps:

[0041] Step 1: Collect the lessor's operational information, including financial information, bad record information, leased product information, and after-sales information;

[0042] Step 2: Based on operational information, analyze the lessor's net profit, bad record score, quality index, and after-sales improvement rate;

[0043] Step 3: Analyze the financial risk level, bad record risk level, quality risk level and after-sales risk level;

[0044] Step 4: Analyze the operational risk level of the lessor and provide a basis for selection for the lessee.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. This law clearly covers the operational values ​​of various operational information, including absolute profit difference, bad record score quality index, and after-sales improvement ratio. It comprehensively evaluates the operational status from multiple dimensions, helps to identify potential risk points, and provides a comprehensive risk management perspective for lessors. By regularly evaluating the risk level of operational information, the operational status of the lessor can be dynamically obtained, providing a basis for judgment when leasing users choose lessors. At the same time, lessors can promptly identify problems and deficiencies in the operational process and take corresponding improvement measures. This continuous improvement mechanism helps to enhance the overall operational level and market competitiveness of the leasing business.

[0047] 2. The present invention analyzes the lessor's net profit, bad record score, quality index and after-sales improvement ratio based on operational information; and further analyzes the financial risk level, bad record risk level, quality risk level and after-sales risk level; analyzes the lessor's operational risk level by comprehensively considering the risk levels of various operational information; and reflects the lessor's overall operational status; in a market environment where there are multiple lessors of the same type, the lessee can make a detailed comparison and evaluation based on the risk status of each lessor to make a choice, effectively avoiding the selection of those leasing service providers with higher potential risks or poor reputation, thereby ensuring that the lessee can enjoy a more secure and reliable service experience during the leasing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 Schematic diagram of the system structure of the present invention;

[0050] Figure 2 Schematic diagram of the risk level analysis method of various operation information of the present invention;

[0051] Figure 3 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0052] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] See also Figure 1 , a leasing risk dynamic assessment system according to a first aspect of the present invention includes a data acquisition module and a risk assessment module;

[0054] The data collection module collects the operating information of the lessor; the operating information includes financial information, bad record information, leased product information and after-sales information;

[0055] The risk assessment module analyzes the net profit of the lessor during the operating cycle and determines whether the net profit can support the operation of the next operating cycle. If not, the module analyzes the financial risk level; if yes, no action is taken.

[0056] Specifically, extracting financial information of the operating cycle; wherein the financial information includes income data and expenditure data;

[0057] Net profit QJL is calculated using the formula QJL = SR - ZC; where SR is revenue and ZC is expenditure;

[0058] Extract the lessor's expenditure data for several days from historical data, average the expenditure data to get the average daily expenditure, and multiply the average daily expenditure by the number of days in the operating cycle to get the expenditure required for the operating cycle; where the operating cycle is measured in days as the smallest unit;

[0059] If the net profit is greater than the expenditure of the operating cycle, it can support the operation of the next operating cycle; otherwise, it cannot support the operation of the next operating cycle.

[0060] For example, assuming a 30-day operating cycle, total monthly revenue of 60,000 yuan, and total expenditure of 23,000 yuan, the net profit is 60,000 yuan - 23,000 yuan = 37,000 yuan. Extract several days of expenditure data from historical data and calculate the daily expenditure, assuming it is 500 yuan / day.

[0061] The expenditure required for the operating cycle is calculated as 500×30=15,000 yuan <37,000 yuan, which means that the net profit of this month can support the operation of the next month.

[0062] The risk assessment module analyzes the bad record score of the lessor based on the bad information; based on the bad record score;

[0063] Specifically, we extracted the behavioral norms on leasing from the Leasing Business Association and scored each behavioral norm according to its importance;

[0064] The bad record score BLF of the lessor during the operation cycle is calculated by the formula BLF=Σ(FSi×Ni); where i is the code of conduct number, i=0, 1,…, M, M is a positive integer; FSi is the score of the violated code of conduct i; Ni is the number of violations of code of conduct i during the operation cycle; where experts score the importance of the code of conduct; and Σ is the sum of i.

[0065] It should be noted that the Leasing Business Association has formulated relevant regulations to govern the behavior of lessors. Experts rated the importance of behavioral norms, such as reasonable charges: clear charges, no unreasonable or hidden charges, ensuring that tenants are aware of every fee they pay; expert rating: 5 / 5 (very important); dispute resolution mechanism: lessors should have an effective dispute resolution mechanism and actively participate in resolving disputes between tenants and lessors; expert rating: 3 / 5 (important).

[0066] The present invention evaluates the degree of irregular behavior of the lessor through the bad record score domain; the degree of irregular behavior is mainly related to the score corresponding to each bad behavior and the number of times each bad behavior occurs during the operating cycle. When the number of bad behaviors with larger scores occurs more frequently during a certain operating cycle, it indicates that the lessor has more serious bad behaviors during the operating cycle, such as problems in serving customers and violations of relevant regulations, which require timely adjustment and improvement.

[0067] The risk assessment module analyzes the damage index of the leased goods based on the leased goods information;

[0068] Extract the fault problems and repair times of the leased products from the historical maintenance records; the experts set the impact coefficient of each fault problem on the leased product, marked as A t , t is the fault problem type;

[0069] By the formula SHD=Σ x Σ t (A xt ×C xt ) calculates the damage index SHD of the rental product; where x is the rental product number; C xt is the number of maintenance times, Σ t Sum over t, Σ x Sums x.

[0070] The present invention evaluates the damage index of leased goods through a damage index; the damage index of a leased good is closely related to the influence coefficient of historical fault problems of the leased good on it, and the corresponding number of repairs; if a leased good has multiple historical fault problems and their influence coefficients on the leased good are all large, and the corresponding number of repairs are all large, then the damage index of the leased good is more serious; if there are multiple leased goods with relatively serious damage, it indicates that the goods of the lessor are all severely damaged, so the damage index is relatively large.

[0071] Furthermore, the current usage period of the rental product and the rated usage period are extracted from the rental product information, and the difference is processed to obtain the usable period, and the number of rental products whose usable period is less than the period threshold is counted;

[0072] By the formula ZLX=e SHD +e QT The quality evaluation index ZLX is calculated, where QT is the number of rental products whose usable period is less than the period threshold.

[0073] The present invention evaluates the quality of leased goods through a quality evaluation index; the quality of leased goods is closely related to the damage index of the leased goods and the usable period of the leased goods; if the damage index is large and the number of leased goods with a usable period less than the period threshold is large, it indicates that the quality of the goods of the lessor is poor, so the quality evaluation index is large.

[0074] The risk assessment module analyzes the lessor's after-sales perfection rate based on after-sales information;

[0075] Specifically, the number of after-sales service items of the lessor is counted and compared with the number of standard after-sales service items to obtain the after-sales perfection ratio.

[0076] It should be noted that by evaluating the after-sales perfection ratio of a lessor, we can know whether the lessor's after-sales service is complete, and then evaluate the quality of its after-sales service.

[0077] See also Figure 2 , analyze the risk level of each operational information, as follows:

[0078] Set K preset thresholds, marked as A r,j ; Where r is the type of operation information; j is the preset threshold number, j = 0, 1, ..., K, K is a positive integer; A r,j Increase successively;

[0079] Compare the operation value of each operation information with the corresponding preset threshold in turn; when the operation value is greater than A r,j and less than A r,j+1 When the operation value is less than A, the risk level of the operation information is Q, and Q = j;r,0 When , the risk level of the operation information is 0; the operation value of the operation information includes the absolute difference in profit, the quality index of bad record score and the after-sales improvement ratio; the absolute value of the profit difference is the absolute difference between the net profit and the profit threshold.

[0080] Among them, the larger the risk level value, the higher the risk level of the corresponding operation information.

[0081] For example, taking bad record information as an example, analyze its risk level; assume that 5 preset thresholds for bad record scores are set, and these thresholds increase in sequence.

[0082] Bad record score quality index (A r,j ): A r,0 =10, A r,1 =34, A r,2 =45, A r,3 =63, A r,4 =89;

[0083] It should be noted that the difference between the preset thresholds can be a fixed difference or a variable difference, depending on the actual situation; and the setting of the preset threshold is based on the risk level reached by the operation value of the operation information;

[0084] If the bad record score in a certain operation cycle is 20, between A r,0 and A r,1 If the bad record score is between , the risk level Q=1.

[0085] Analyze the lessor's operational risk level;

[0086] By the formula YFD=Σ(H r ×A r,j ) calculates the lessor's operational risk level YFD; where H r is the weight coefficient corresponding to the operation information, H r >0 and ΣH r =1;Σsum r.

[0087] The present invention evaluates the risk level of a lessor through the operational risk level; the risk level of a lessor is closely related to the risk level of each piece of operational information and the corresponding weight coefficient; if the risk level of each piece of operational information is high, it indicates that the risk of the lessor is relatively high, and therefore the operational risk level is relatively high, that is, there are more problems, and the cost-effectiveness is lower than that of other lessors of the same type.

[0088] See also Figure 3 The second aspect of the present invention proposes a dynamic assessment method for lease risk, comprising the following steps:

[0089] Step 1: Collect the lessor's operational information, including financial information, bad record information, leased product information, and after-sales information;

[0090] Step 2: Based on operational information, analyze the lessor's net profit, bad record score, quality index, and after-sales improvement rate;

[0091] Step 3: Analyze the financial risk level, bad record risk level, quality risk level and after-sales risk level;

[0092] Step 4: Analyze the operational risk level of the lessor and provide a basis for selection for the lessee.

[0093] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0094] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The dynamic evaluation system for leasing risks is characterized by: Including data collection module and risk assessment module; Data collection module: used to collect the lessor's operational information; the operational information includes financial information, bad record information, leased product information and after-sales information; Risk assessment module: used to analyze the operational risk level of the lessor; the operational risk level is obtained based on the risk level analysis of various operational information; the risk levels of various operational information are respectively financial risk level, bad record risk level, quality risk level and after-sales risk level; and, Analyze the net profit of the lessor during the operation cycle and determine whether the net profit can support the operation of the next operation cycle; if not, analyze the financial risk level; if yes, do nothing; Analyze the bad record score of the lessor based on the bad information; analyze the bad record risk level based on the bad record score; Extract the usage period of the leased goods and analyze the damage index of the leased goods based on the leased goods information; analyze the quality index of the leased goods based on the usage period and the damage index; analyze the quality risk level based on the quality index; Analyze the after-sales perfection ratio of the lessor based on after-sales information; and analyze the after-sales risk level.

2. The dynamic evaluation system for leasing risk according to claim 1 is characterized in that: The analysis of the net profit of the lessor during the operation cycle and the determination of whether the net profit can support the operation of the next operation cycle include: Extracting financial information of the operating cycle; wherein the financial information includes revenue data and expenditure data; The net profit QJL is calculated by the formula QJL = SR-ZC; where SR is the income and ZC is the expenditure; Extract the expenditure data of several days of the lessor from the historical data, and perform mean processing on the expenditure data to obtain the average daily expenditure. The average daily expenditure is multiplied by the number of days in the operation cycle to obtain the expenditure required for the operation cycle; where the operation cycle is based on days as the smallest unit; If the net profit is greater than the expenditure of the operating cycle, it can support the operation of the next operating cycle; otherwise, it cannot support the operation of the next operating cycle.

3. The dynamic evaluation system for leasing risk according to claim 1 is characterized in that: The bad record score of the lessor based on bad information analysis includes: Extract the leasing codes of conduct from the Leasing Business Association and rate each code of conduct according to its importance; The bad record score BLF of the lessor during the operation cycle is calculated by the formula BLF=Σ(FSi×Ni); wherein i is the code of conduct number, i=0, 1,…, M, M is a positive integer; FSi is the score of the violated code of conduct i; Ni is the number of violations of code of conduct i during the operation cycle; wherein the experts score the importance of the code of conduct; and Σ is the sum of i.

4. The dynamic evaluation system for rental risk according to claim 1 is characterized in that: Based on the rental product information, analyzing the damage index of the rental product includes: Extract the fault problems and maintenance times of the leased products from the historical maintenance records; experts set the impact coefficient of each fault problem on the leased product, marked as A t , t is the fault problem type; By the formula SHD=Σ x Σ t (A xt ×C xt ) calculates the damage index SHD of the rental product; where x is the rental product number; C xt is the number of maintenance times, Σ t Sum over t, Σ x Summing x.

5. The dynamic evaluation system for leasing risk according to claim 4 is characterized in that: The analysis of the quality index of the leased goods includes: Extract the current usage period of the rental product from the rental product information and perform difference processing with the rated usage period to obtain the usable period, and count the number of rental products whose usable period is less than the period threshold; By the formula ZLX = e SHD +e QT The quality evaluation index ZLX is calculated, wherein QT is the number of rental products whose usable period is less than the period threshold.

6. The dynamic evaluation system for leasing risk according to claim 1 is characterized in that: The analysis of the lessor's after-sales perfection ratio based on after-sales information includes: The number of after-sales service items of the lessor is counted and compared with the number of standard after-sales service items to obtain the after-sales perfection ratio.

7. The dynamic evaluation system for leasing risk according to claim 1 is characterized in that: Analyze the risk level of each operational information, including: Set K preset thresholds, marked as A r,j ; where r is the type of operation information; j is the preset threshold number, j = 0, 1, ..., K, K is a positive integer; A r,j Increase successively; Compare the operation value of each operation information with the corresponding preset threshold in turn; when the operation value is greater than A r,j and less than A r,j+1 When the operation value is less than A, the risk level of the operation information is Q, and Q = j; r,0 When , the risk level of the operation information is 0; the operation value of the operation information includes the absolute difference in profit, the quality index of bad record score and the after-sales improvement ratio; the absolute value of the profit difference is the absolute difference between the net profit and the profit threshold.

8. The dynamic evaluation system for rental risk according to claim 7 is characterized in that: The analysis of the lessor's operational risk level includes: By the formula YFD=Σ(H r ×A r,j ) to calculate the lessor's operational risk level YFD; where H r is the weight coefficient corresponding to the operation information, H r >0 and ΣH r =1;Σsum r.

9. A method for dynamic assessment of leasing risk, based on the dynamic assessment system for leasing risk according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Collect the lessor's operational information, including financial information, bad record information, leased product information and after-sales information; Step 2: Based on the operational information, analyze the lessor’s net profit, bad record score, quality index and after-sales improvement ratio; Step 3: Analyze the financial risk level, bad record risk level, quality risk level and after-sales risk level; Step 4: Analyze the operational risk level of the lessor and provide a basis for selection for the lessee.

Images