Shipping rental platform based on expected credit loss model

Abstract

The invention belongs to the technical field of digital platforms, and provides a shipping rental platform based on an expected credit loss model. By arranging the overdue management unit, real-time monitoring of performance of the rental contract is realized, and overdue events can be found and processed in time; five-level classification is performed on the shipping and financing lease contract according to the overdue data, so that the specific risk condition of the contract can be clearly known; credit value reduction stage division is carried out by combining five-stage classification and manual division results, subjective and objective factors are integrated, and judgment is more accurate; the counting and extracting mode is judged according to the stage division result, so that the shipping rental platform can adjust the counting and extracting mode in time; the expected credit loss model is determined according to the calculation and extraction mode, so that the expected credit loss evaluation is more flexible, and the possible credit risk is predicted in advance by calculating the expected credit loss; pD and LGD parameters are manually set, and the model lag underestimation risk is avoided; and by means of the report management module, expected credit reduction summary data is visually presented, so that a manager of a shipping and renting company can analyze and compare conveniently.

Description

Technical Field

[0001] The present invention relates to the technical field of digital platforms, and particularly to a shipping lease platform based on an expected credit loss model. Background Art

[0002] Shipping lease platforms have functions such as the management of shipping industry financing lease contracts, lease progress tracking, and payment management, which can ensure the transparency and standardization of the leasing process. However, in the current operation practice of shipping lease platforms, there is a lack of the ability to assess the default risk of tenants. Specifically, when a tenant's finances have hidden problems but have not been significantly revealed, traditional assessment indicators often struggle to accurately capture their potential default risk, resulting in shipping lease companies potentially reaching deals with high-risk tenants under insufficient information. More critically, existing platforms lack the ability to predict risks prospectively and cannot anticipate in advance the impact of an overall industry downturn on the credit status of tenants. Thus, once a tenant's business deteriorates, shipping lease companies will be in a passive position due to their failure to timely detect the credit impact of industry risks and will have difficulty effectively coping with sudden risk challenges. Summary of the Invention

[0003] In order to solve the problems in the prior art that shipping lease platforms lack the ability to assess the default risk of tenants and lack the ability to predict risks prospectively, a shipping lease platform based on an expected credit loss model is proposed to solve the above problems.

[0004] A shipping lease platform based on an expected credit loss model includes:

[0005] A financing lease contract management module, including: a contract management unit for performing operations such as the production, finalization, signing, lease commencement, and change of shipping industry financing lease contracts; a collection management unit for recording actual collection information and matching and writing off the accounts receivable of shipping industry financing lease contracts; an overdue management unit for comparing the accounts receivable of the shipping industry financing lease contracts with the corresponding actual received accounts in the collection management unit to obtain the overdue data of the shipping industry financing lease contracts.

[0006] A contract credit five-level classification module: judging the current level to which the shipping industry financing lease contract belongs based on the overdue data; the levels include: "normal", "watch", "substandard", "doubtful", and "loss".

[0007] Expected Credit Impairment Detail Maintenance Module: It includes: an expected credit impairment query unit and an expected credit impairment detail unit; the expected credit impairment query unit queries the expected credit impairment detail report based on any one of the query conditions of the assessment number, query number, or lessee customer; the expected credit impairment detail unit stores the expected credit impairment detail report, and the expected credit impairment detail report includes, but is not limited to: the affiliated institution, contract number, lease start date, lease expiration date, lessee name, industry, contract interest rate, currency, remaining margin, remaining principal, five-level classification, overdue days, guarantee method, stage division - by five-level classification, stage division - before other manual adjustments, stage division - after other manual adjustments, final stage division - consolidation of the same customer, provisioning method, probability of default (PD), loss given default (LGD), exposure at default, individual expected credit loss, portfolio expected credit loss, and final expected credit loss;

[0008] Report Management Module, which is used to statistically summarize the expected credit impairment data in the form of a report. The expected credit impairment summary data includes: the remaining principal of the shipping industry finance lease contract during the corresponding period in the asset detailed list, the expected credit loss of the tenant, and the provisioning ratio;

[0009] User Interaction Interface: It is used to approve the expected credit impairment status of the tenant based on the expected credit loss of the Report Management Module and visually display the report of the Report Management Module.

[0010] Preferably, the contract number, lease start date, lease expiration date, lessee name, industry, contract interest rate, and currency in the expected credit impairment detail report are obtained by parsing the shipping industry finance lease contract.

[0011] Preferably, the expected credit impairment detail maintenance module further includes a credit impairment calculation unit, which is used to calculate the exposure at default, individual expected credit loss, portfolio expected credit loss, and final expected credit loss in the expected credit impairment detail report.

[0012] Preferably, the credit impairment calculation unit includes:

[0013] Classify the credit impairment stage of the tenant based on the current classification level of the tenant's shipping industry finance lease contract;

[0014] Judge the provisioning method based on the credit impairment stage division result;

[0015] Obtain the expected credit loss based on the provisioning method; the provisioning methods include: individual provisioning and portfolio provisioning.

[0016] Preferably, when the provisioning method is single provisioning, the individual expected credit loss is calculated using the formula: ECL = K, where ECL represents the expected credit loss and K is a fixed ratio; when the provisioning method is grouped, the grouped expected credit loss is calculated using the formula: ECL = PD * LGD * EAD, where PD represents the probability of default, LGD represents the loss given default, and EAD represents the exposure at default; the expected credit loss calculated according to the provisioning method is used as the final expected credit loss.

[0017] Preferably, the exposure at default (EAD) in the detailed statement of expected credit impairment is obtained by subtracting the remaining margin from the remaining principal; if the impairment is less than or equal to 0, EAD is 0; the remaining principal is obtained by subtracting the received accounts from the accounts receivable corresponding to the shipping industry finance lease contract.

[0018] Preferably, the credit impairment stage of the tenant is classified based on the current classification level of the tenant's shipping industry finance lease contract, including:

[0019] The stage classification - by five - level classification, the credit impairment stage is classified according to the current five - level classification result of the shipping industry finance lease contract;

[0020] The stage classification - before other manual adjustments, the credit impairment stage of the shipping lease contract is manually adjusted according to the overdue data;

[0021] The stage classification - after other manual adjustments, the credit impairment stage is adjusted by comparing the classification results of the stage classification - by five - level classification and the stage classification - before other manual adjustments;

[0022] The final stage classification - consolidation for the same customer, by comparing the adjusted credit impairment stage results of all contracts under the same tenant, the highest credit impairment stage is used as the final credit impairment stage of all contracts under the tenant.

[0023] Preferably, the method for classifying the credit impairment stage according to the five - level classification result is as follows: when the five - level classification result is "normal", the value is 1; when the five - level classification result is "watch", the value is 2; when the five - level classification result is "substandard", "doubtful", or "loss", the value is 3.

[0024] Preferably, the comparison method in the adjustment of the credit impairment stage is that when the classification result before other manual adjustments is not empty, the output result is the corresponding classification result before other manual adjustments, otherwise the output result is the corresponding classification result of the stage classification - by five - level classification.

[0025] Preferably, the report management module further includes: a cycle management unit for statistically analyzing report data for different time periods; a tenant management unit for statistically analyzing report data by tenant; an accrual management unit for statistically analyzing report data by accrual method; and an industry management unit for statistically analyzing report data by industry classification.

[0026] Preferably, the data statistically analyzed by the report management module further includes: the remaining principal, ECL, and accrual ratio in dimensions of "final stage division - same customer consolidation", "accrual method", and "industry"; the calculation formula for the accrual ratio is: accrual ratio = ECL / remaining principal.

[0027] Preferably, the user interaction interface further includes:

[0028] a new unit for triggering the latest expected credit impairment calculation process;

[0029] an export unit for exporting the expected credit impairment summary data in the report management module;

[0030] a jump link unit for entering the tenant expected credit impairment details maintenance module;

[0031] a submission for approval unit for entering the expected credit impairment approval process;

[0032] a deletion unit for deleting the expected credit impairment documents with the approval status of "new" or "approval rejected" for expected credit impairment;

[0033] an expected credit parameter setting unit for filling in the probability of default (PD) and loss given default (LGD) in the expected credit loss filled in by the tenant.

[0034] Preferably, the expected credit parameter setting unit includes:

[0035] manually filling in the values of the probability of default for different stages and different years based on the contract information of the contract management unit and the macroeconomic indicators;

[0036] manually filling in the values of the loss given default for different categories based on the code values of the categories and the loan recovery rate; the code values of the categories include: secured and unsecured.

[0037] Beneficial effects:

[0038] The present invention provides a shipping leasing platform based on an expected credit loss model, which improves the risk prediction ability of the shipping leasing platform, enhances financial soundness and transparency by prospectively evaluating the probability of tenant default, meets regulatory compliance requirements at the same time, and promotes sustainable development; in addition, it fully meets the credit risk management needs of the shipping industry leasing, which is a long-cycle and high-risk industry.

[0039] Meanwhile, by setting up an overdue management unit to implement the overdue management function, the platform can monitor the performance of lease contracts in real time, promptly discover and handle overdue events; input the overdue data obtained from the overdue management unit into the contract credit five-level classification module to classify the contracts of current tenants into five levels, enabling a clear understanding of the specific risk status of each contract, providing more accurate data support for subsequent risk management decisions, and simultaneously being able to promptly discover contracts with gradually increasing risks according to the classification situation and take effective measures to avoid risks;

[0040] Furthermore, the expected credit impairment details maintenance module of the present invention realizes the division of credit impairment stages based on the classification results of the contract credit five-level classification module and the manual stage division results, comprehensively considers objective and subjective factors for stage division, and achieves a more accurate judgment; by constructing a method for judging the provisioning method according to the stage division results, the shipping lease platform can promptly adjust the provisioning method to adapt to the constantly changing risk environment; determine the expected credit loss model according to the provisioning method, and can promptly adjust the provisioning ratio and method according to the changes in risks, making the assessment of expected credit losses more flexible and sensitive; by calculating the expected credit losses, potential credit risks can be predicted in advance, avoiding drastic fluctuations in financial results due to sudden defaults; by manually setting the PD parameters and LGD parameters, the parameters can be quickly adjusted in real time according to the changing market information, avoiding the risk underestimation caused by the lag of the model; through the report management module, the summary data of the statistical expected credit impairment can be presented intuitively, facilitating the analysis and comparison by the management of the shipping lease company, helping the shipping lease company to promptly discover potential risks and problems, and taking corresponding measures to deal with them. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is an architecture diagram of a shipping lease platform based on an expected credit loss model.

[0042] Figure 2 It is a schematic diagram of an expected credit impairment interface.

[0043] Figure 3 It is a schematic diagram of an expected credit impairment details maintenance table in the expected credit impairment details unit.

[0044] Figure 4 It is a schematic diagram of an overdue weekly report generated by the overdue management unit.

[0045] Figure 5 It is a schematic diagram of the contract credit five-level classification module.

[0046] Figure 6 It is a schematic diagram of the interface of the expected credit parameter setting unit.

[0047] Figure 7It is a schematic diagram of the interface of the expected credit impairment summary report module.

[0048] Figure 8 It is a schematic diagram of the Excel content for exporting the expected credit impairment summary data. Specific implementation manners

[0049] The following will be further described in conjunction with specific embodiments.

[0050] As Figure 1 shown, a shipping lease platform based on the expected credit loss model includes:

[0051] The financing lease contract management module includes: a contract management unit for performing operations such as making, finalizing, signing, starting the lease, and changing business operations on the financing lease contracts in the shipping industry; a collection management unit for recording actual collection information and matching and writing off the accounts receivable of the financing lease contracts in the shipping industry; an overdue management unit for comparing the accounts receivable of the financing lease contracts in the shipping industry with the corresponding actual received accounts in the collection management unit to obtain the overdue data of the financing lease contracts in the shipping industry.

[0052] Specifically, as Figure 4 shown is the overdue weekly report generated by the overdue management unit, which includes the actual received amount, overdue amount, overdue grade, and historical overdue days of each financing lease contract in the shipping industry. The contract credit five-level classification module will judge the five-level classification result of the financing lease contract in the shipping industry based on the historical overdue days;

[0053] The contract credit five-level classification module: judging the current level to which the financing lease contract in the shipping industry belongs based on the overdue data; the levels include: "normal", "watch", "substandard", "doubtful", and "loss";

[0054] Specifically, the five-level classification is statistically calculated once every quarter and is classified according to the overdue days. As Figure 5 shown is the five-level classification result of each shipping lease contract in the fourth quarter of 2024. Among the five-level classification results of all shipping lease contracts in the fourth quarter of 2024, only one shipping lease contract is in the watch category, and the others are all in the normal category;

[0055] The five-level classification standard for the fourth quarter of 2024 is:

[0056] Level 1: Normal category

[0057] The overdue days are within 30 days (including 30 days); among them, the overdue days within 7 days (including 7 days) are normal level 1; in case of overdue, the overdue days do not exceed 30 days (including 30 days) are normal level 2.

[0058] Level 2: Watch category

[0059] The number of overdue days is between 31 and 90 days (including 90 days).

[0060] Level 3: Substandard

[0061] The number of overdue days is between 91 and 180 days (including 180 days).

[0062] Level 4: Doubtful

[0063] The number of overdue days is between 181 and 365 / 366 days (including 365 / 366 days).

[0064] Level 5: Loss

[0065] The number of overdue days exceeds 365 / 366 days.

[0066] Expected credit impairment details maintenance module: includes: expected credit impairment query unit and expected credit impairment details unit; the expected credit impairment query unit queries the expected credit impairment details report based on any one of the evaluation number, query number or lessee customer; the expected credit impairment details unit stores the expected credit impairment details report, and the expected credit impairment details report includes but is not limited to: affiliated institution, contract number, lease start date, lease expiration date, lessee name, industry, contract interest rate, currency, remaining margin, remaining principal, five-level classification, number of overdue days, guarantee method, stage division - by five-level classification, stage division - before other manual adjustments, stage division - after other manual adjustments, final stage division - consolidation of the same customer, provisioning method, probability of default, loss given default, exposure at default, individual expected credit loss, portfolio expected credit loss and final expected credit loss;

[0067] Report management module, used to statistically summarize the expected credit impairment data in the form of a report, and the expected credit impairment summary data includes: the remaining principal of the shipping industry finance lease contract during the corresponding period in the asset details table, the expected credit loss of the tenant and the provisioning ratio;

[0068] User interaction interface: used to approve the expected credit impairment status of the tenant based on the expected credit loss of the report management module and visually display the report of the report management module.

[0069] Such as Figure 2As shown, the interactive interface can add a quarter's expected credit impairment for maintenance by clicking the Add button, where the expected number is automatically generated by the system; the creator and approval status are automatically displayed; the expected year, month, quarter, and date are maintained by the user; at the same time, the expected credit impairment interface will automatically generate a hyperlink to the credit details. Click the credit details hyperlink to enter the interface of the expected credit impairment details maintenance module; and by clicking the [Export] button on the expected credit impairment interface, you can export the expected impairment summary data for the corresponding expected year and month and the corresponding company's PD parameters and LGD parameters, and export the excel report. The content in the exported excel is as follows: Figure 8 shown.

[0070] Functional description of expected credit impairment interface:

[0071] 1) Add: Click the [Add] button to add expected credit data for maintenance;

[0072] 2) Delete: Select the document and click the [Delete] button to delete the document with the status of "New" or "Approval Rejected";

[0073] 3) Submit for approval: Select the document and click the [Submit for Approval] button to enter the expected credit impairment approval process;

[0074] 4) Save: Click the [Save] button to save the data;

[0075] 5) Attachment upload: Click the [Attachment Upload] button to upload the attachment information.

[0076] like Figure 3 As shown, the information in the expected credit impairment detail maintenance detail table includes:

[0077] 1) Affiliate: drop-down box, read-only, the system automatically brings out contract details-basic information-affiliated company;

[0078] 2) Contract number: text box, read-only, the system automatically finds the leased, pending sale and leaseback, and equipment leasing contracts (users can manually delete contracts that do not require expected credit impairment);

[0079] 3) Lease start date: Date box, read-only, the system automatically brings up the contract details-basic information-lease start date;

[0080] 4) Lease expiration date: date box, read-only, the system automatically brings out contract details-basic information-end date;

[0081] 5) Tenant name: text box, read-only, the system automatically brings up contract details-transaction object-primary tenant-customer name;

[0082] 6) Industry: Drop-down box, read-only, automatically filled by the system with the project type in the contract details - basic information;

[0083] 7) Contract interest rate: Numeric box, read-only, automatically filled by the system with the annualized interest rate in the contract details - quotation plan;

[0084] 8) Currency: Drop-down box, read-only, automatically filled by the system with the currency type in the contract details - quotation plan;

[0085] 9) Remaining margin: Drop-down box, read-only, automatically filled by the system with the remaining margin of the contract for the corresponding period in the asset detailed list;

[0086] 10) Remaining principal (local currency): Drop-down box, read-only, automatically filled by the system with the remaining principal of the contract for the corresponding period in the asset detailed list;

[0087] 11) Five-category classification: Drop-down box, read-only, automatically filled by the system with the five-category classification result of the previous quarter of the contract;

[0088] 12) Days overdue: Numeric box, read-only, automatically filled by the system with the historical days overdue at the current node of the contract overdue weekly report;

[0089] 13) Guarantee method: Drop-down box, read-only, automatically filled by the system with the contract guarantee method, i.e., ship-related business is regarded as guaranteed; non-ship-related business is regarded as unguaranteed;

[0090] 14) Stage division - by five-category classification: Numeric box, read-only, automatically filled by the system. When the five-category classification result is "normal level 1" or "normal level 2", the value is 1; when the five-category classification result is "watch", the value is 2; when the five-category classification result is "substandard", "doubtful", or "loss", the value is 3;

[0091] 15) Stage division - other manual adjustment: Numeric box, editable, users can make other risk adjustments according to the risk situation;

[0092] 16) Stage division - after manual adjustment: Numeric box, read-only, automatically filled by the system. When "Stage division - other manual adjustment" is not empty, the value is "Stage division - other manual adjustment"; otherwise, the value is "Stage division - by five-category classification";

[0093] 17) Final stage division - consolidation for the same customer: Numeric box, read-only, automatically filled by the system. If there are two or more contracts under a certain lessee customer, the highest value of the "Stage division - after manual adjustment" level of that lessee customer is taken;

[0094] 18) Accrual method: Drop-down box, read-only, automatically filled by the system, code values include "single item" and "combination". When "Final stage division - Merging of the same customer" is "1", the accrual method is "combination"; when "Final stage division - Merging of the same customer" is not "1", the accrual method is "single item".

[0095] 19) PD used: Numeric box, required. When the accrual method is "combination", the system automatically fills in the corresponding company PD value maintained by the user in the PD parameter function; when the accrual method is "single item", the user needs to fill it in by themselves; PD represents the probability of default.

[0096] 20) LGD: Numeric box, read-only. When the five-category classification is "loss", the value of this field is "100%"; when the five-category classification is not "loss", it is judged according to the guarantee method, that is, if the guarantee method is "guaranteed", the corresponding guaranteed LGD parameter maintained by the user in the PD parameter function is taken; if the guarantee method is "unsecured", the corresponding unsecured LGD parameter maintained by the user in the PD parameter function is taken; LGD represents the loss given default.

[0097] 21) EAD: Numeric box, read-only, with the value being the remaining principal (domestic currency) minus the remaining margin. If the impairment is less than or equal to 0, then 0 is taken; EAD represents the exposure at default.

[0098] 22) ECL (single item): Numeric box, editable, filled in by the user themselves; ECL represents the expected credit loss.

[0099] 23) ECL (combination): Numeric box, read-only, automatically filled in by the system with PD * LGD * EAD.

[0100] 24) Final ECL: Numeric box, read-only. When the accrual method is "single item", it takes the value of "ECL (single item)"; when the accrual method is "combination", it takes the value of "ECL (combination)".

[0101] The expected credit parameter setting unit can add companies and categories through the [New] button to set the corresponding PD values and LGD parameters. The set values will be associated with the PD values and LGD values at the expected credit impairment in the expected credit impairment details maintenance module, including:

[0102] Button descriptions on the page of the expected credit parameter setting unit:

[0103] 1) New: Click the [New] button to add and maintain expected credit data.

[0104] 2) Delete: Check the document and click the [Delete] button to delete the document.

[0105] 3) Save: Click the [Save] button to save the data.

[0106] Field Descriptions of the Expected Credit Parameter Setting Unit Page:

[0107] 1) Company: Drop-down box, required. The code values include the full company name defined at the company (lessor) to which the document belongs.

[0108] 2) PD Value: Numeric box, required. The user maintains it independently, and it will be associated with the PD field in the expected credit impairment details maintenance module according to the company.

[0109] 3) Category: Drop-down box, required. The code values include secured and unsecured.

[0110] 4) LGD Parameter: Numeric box, required. The user maintains it independently, and it will be associated with the LGD field in the expected credit impairment details maintenance module according to the company.

[0111] Specifically, as Figure 6 shown, the default probability of default (PD) values for the first stage of each tenant are set in the expected credit impairment parameter setting unit, and at the same time, the PD values corresponding to each year in different years of signing the shipping finance lease contract are clarified. In addition, the corresponding loss given default (LGD) parameters are set for both secured and unsecured cases.

[0112] The page of the report management module displays the summary data of the remaining principal, ECL, and provision ratio of each tenant in terms of "stage", "provision method", and "industry". The stage is the final stage division - consolidation of the same customer; at the same time, the summary data of the expected credit impairment approved in the latest period is displayed by default; the summary data of the expected credit impairment for the corresponding period can be searched by entering the query period; specifically, the provision ratio is obtained by dividing the expected credit impairment amount by the remaining principal.

[0113] Description of the Page Buttons of the Report Management Module:

[0114] 1) Query: Click the [Query] button after entering the conditions to query the data.

[0115] 2) Reset: Clear the query conditions.

[0116] Field Descriptions of the Page of the Report Management Module:

[0117] 1) Period: Value list, inputtable. The user enters it independently.

[0118] Specifically, as Figure 7 shown, the summary data of the expected credit impairment approved in the latest period of a certain tenant is displayed, which summarizes the remaining principal, ECL, and provision ratio of the tenant in the first credit impairment stage, with the provision method being a combination, and the industry type being ship assets; among them, the provision ratio is obtained by dividing the ECL by the remaining principal.

[0119] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred arrangement, those of ordinary skill in the art should understand that the technical solution of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A shipping leasing platform based on an expected credit loss model, characterized in that: include: The finance lease contract management module includes: including but not limited to a contract management unit for making, finalizing, signing, starting leases, and changing business operations for shipping industry finance lease contracts; a collection management unit for recording actual collection information and matching and writing off accounts receivable of shipping industry finance lease contracts; an overdue management unit for comparing accounts receivable of the shipping industry finance lease contract with the corresponding actual collection in the collection management unit to obtain overdue data of the shipping industry finance lease contract; Contract credit five-level classification module: based on the overdue data, the current level of the shipping industry finance lease contract is determined; the levels include: "normal", "special attention", "substandard", "doubtful" and "loss"; Expected credit impairment detail maintenance module: including: expected credit impairment query unit and expected credit impairment detail unit; the expected credit impairment query unit queries the expected credit impairment detail report based on the assessment number, query number or any query condition of the lessee; the expected credit impairment detail unit stores the expected credit impairment detail report, which includes but is not limited to: affiliated institution, contract number, lease start date, lease expiration date, lessee name, industry, contract interest rate, currency, remaining margin, remaining principal, five-level classification, overdue days, guarantee method, stage division-by five-level classification, stage division-before other manual adjustments, stage division-after other manual adjustments, final stage division-same customer merger, provisioning method, default probability, default loss rate, default risk exposure, single expected credit loss, combined expected credit loss and final expected credit loss; A report management module, used to compile statistics of expected credit impairment summary data in the form of reports, wherein the expected credit impairment summary data includes: the remaining principal of the shipping industry financial lease contract in the corresponding period in the asset statement, the expected credit loss of the tenant and the provision ratio; User interaction interface: used to approve the expected credit impairment status of the tenant based on the expected credit loss of the report management module and to visualize the report of the report management module.

2. A shipping leasing platform based on an expected credit loss model according to claim 1, characterized in that: The contract number, lease commencement date, lease expiration date, lessee name, industry, contract interest rate and currency in the expected credit impairment detail report are obtained by parsing the shipping industry finance lease contract.

3. A shipping leasing platform based on an expected credit loss model according to claim 1, characterized in that: The expected credit impairment detail maintenance module also includes a credit impairment calculation unit, which is used to calculate the default risk exposure, single expected credit loss, combined expected credit loss and final expected credit loss in the expected credit impairment detail report.

4. A shipping leasing platform based on an expected credit loss model according to claim 3, characterized in that: The credit impairment calculation unit comprises: Classify the credit impairment stage of the tenant based on the current grade of the shipping industry finance lease contract of the tenant; Determine the provision method based on the credit impairment stage classification results; The expected credit losses are obtained based on the provisioning method; the provisioning methods include: single provision and combination.

5. A shipping leasing platform based on an expected credit loss model according to claim 3 or 4, characterized in that: When the provision method is single provision, the single expected credit loss is calculated using the formula: ECL=K, where ECL represents the expected credit loss and K is a fixed ratio; when the provision method is combined provision, the combined expected credit loss is calculated using the formula: ECL=PD*LGD*EAD, where PD represents the probability of default, LGD represents the default loss rate, and EAD represents the default risk exposure; the expected credit loss calculated according to the provision method is taken as the final expected credit loss.

6. A shipping leasing platform based on an expected credit loss model according to claim 1, characterized in that: The exposure at default, or EAD, in the expected credit impairment breakdown is obtained by subtracting the remaining principal from the remaining margin; if the impairment is less than or equal to 0, the EAD is 0; the remaining principal is obtained by subtracting the accounts receivable from the actual accounts received under the shipping industry finance lease contract.

7. A shipping leasing platform based on an expected credit loss model according to claim 1 or 4, characterized in that: The credit impairment stage of the tenant is classified based on the current grade of the shipping finance lease contract of the tenant, including: The stage classification - according to the five-level classification, the credit impairment stage is divided according to the five-level classification results to which the shipping industry finance lease contract currently belongs; The stage division - before other manual adjustments, the shipping lease contract is manually adjusted for credit impairment based on the overdue data; After the said stage division - other manual adjustments, the credit impairment stage is adjusted by comparing the classification results of the said stage division - five-level classification with the classification results of the said stage division - before the said stage division - other manual adjustments; The final stage division - same customer consolidation is achieved by comparing the adjusted credit impairment stage results of all contracts under the same tenant, and taking the highest credit impairment stage as the final credit impairment stage of all contracts under the tenant.

8. A shipping leasing platform based on an expected credit loss model according to claim 7, characterized in that: The method of dividing the credit impairment stages according to the five-level classification results is: when the five-level classification result is "normal", the value is 1; when the five-level classification result is "concern", the value is 2; when the five-level classification result is "substandard", "doubtful" or "loss", the value is 3.

9. A shipping leasing platform based on an expected credit loss model according to claim 7, characterized in that: The comparison method in the credit impairment adjustment stage is that when the stage division - the division result before other manual adjustments is not empty, the output result is the stage division - the corresponding division result before other manual adjustments; otherwise, the output result is the stage division - the corresponding division result according to the five-level classification.

10. A shipping leasing platform based on an expected credit loss model according to claim 1, characterized in that: The report management module also includes: a period management unit for counting report data in different time periods; a tenant management unit for counting report data by tenant; an accrual management unit for counting report data by accrual method; and an industry management unit for counting report data by industry classification.

11. A shipping leasing platform based on an expected credit loss model according to claim 1, characterized in that: The data counted by the report management module also includes: the remaining principal, ECL, and provision ratio based on the dimensions of "final stage division-merger of the same customer", "provision method", and "industry"; the provision ratio calculation formula is: provision ratio = ECL / remaining principal.

12. A shipping leasing platform based on an expected credit loss model according to claim 1, characterized in that: The user interaction interface also includes: A new unit for triggering the latest expected credit impairment calculation process; An export unit for exporting the expected credit impairment summary data in the report management module; A jump link unit for entering the tenant's expected credit impairment detail maintenance module; Submission approval unit for entering the expected credit impairment approval process; Delete the deletion unit of expected credit impairment documents whose approval status is New or Approval Rejected; An expected credit parameter setting unit used to fill in the expected credit loss of the tenant, namely the probability of default (PD) and the loss given default (LGD).

13. A shipping leasing platform based on an expected credit loss model according to claim 12, characterized in that: The expected credit parameter setting unit includes: Manually fill in the values ​​of default probability parameters at different stages and years based on the contract information and macroeconomic indicators of the contract management unit; Manually fill in the values ​​of default loss rates of different categories based on the code values ​​of the categories and the loan recovery rate; the code values ​​of the categories include: secured and unsecured.

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