CN111127197A - Foreign trade supply chain financial risk control method - Google Patents
Foreign trade supply chain financial risk control method Download PDFInfo
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- CN111127197A CN111127197A CN201911424829.3A CN201911424829A CN111127197A CN 111127197 A CN111127197 A CN 111127197A CN 201911424829 A CN201911424829 A CN 201911424829A CN 111127197 A CN111127197 A CN 111127197A
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- 238000012954 risk control Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000013068 supply chain management Methods 0.000 claims abstract description 10
- 238000007405 data analysis Methods 0.000 claims abstract description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000007637 random forest analysis Methods 0.000 claims description 4
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- 238000012545 processing Methods 0.000 abstract description 4
- 230000008569 process Effects 0.000 description 6
- 238000012546 transfer Methods 0.000 description 5
- 238000013523 data management Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
Abstract
The invention provides a foreign trade supply chain financial risk control method, which comprises the following steps: establishing a risk model, acquiring various risk data from a supply chain management platform, setting early warning ranges under various risk scenes based on a quantitative risk model of big data analysis, namely risk index data threshold values; retraining the risk model to obtain a retrained risk model when input data of the risk model changes; acquiring a customer information list from a supply chain management platform, distributing credit limits for customers in the customer information list one by one, and judging whether the current charged limit of the customer exceeds the authorized limit; if yes, automatically carrying out early warning, and executing the operation of limiting the use of financial services for the customer; if not, classifying according to the customer requirements, and carrying out risk control corresponding to the early warning threshold value under the risk model. The invention is convenient and quick, can ensure the timely processing of abnormal conditions and ensure the effectiveness and the realizability of the credit supporting assets.
Description
Technical Field
The invention belongs to the technical field of supply chain management, and particularly relates to a financial risk control method for a foreign trade supply chain.
Background
The key point of traditional supply chain financial risk control is to pay attention to the core enterprise, pay attention to the property or property right mortgage, accounts receivable pledge and the like, and pay attention to the transferability or compensability of the risk. The risk control method comprises risk avoidance, risk transfer, risk self-retention and risk compensation, loss control and the like. Risk avoidance is the refusal of credit to enterprises which cannot meet the bank risk bearing capacity, and banks should set admission credit rating bottom lines for core enterprises and supply chain member enterprises respectively, and set admission thresholds for different products. The risk transfer not only comprises credit risk transfer tools such as export credit insurance and receivable account insurance, but also comprises risk transfer of introducing non-banking businesses such as third-party logistics enterprises in the logistics link. In supply chain financial business, banks should perfect risk metering system and establish risk compensation mechanism closely related to risk pricing. In the supply chain financial practice, because numerous logistics links are involved, the operation complexity is far higher than that of the traditional mobile fund loan service, and therefore, great attention must be paid to the operation risk control in the supply chain financial wind control to ensure the effectiveness and the realizability of the credit supporting assets.
Disclosure of Invention
The invention aims to provide a financial risk control method for a foreign trade supply chain, which is convenient and quick, ensures timely processing of abnormal conditions and ensures the effectiveness and the realizability of credit supporting assets.
The invention provides the following technical scheme:
a method of foreign trade supply chain financial risk control comprising the steps of:
establishing a risk model, acquiring various risk data from a supply chain management platform, setting early warning ranges under various risk scenes based on a quantitative risk model of big data analysis, namely risk index data threshold values;
retraining the risk model to obtain a retrained risk model when input data of the risk model changes;
acquiring a customer information list from a supply chain management platform, distributing credit limits for customers in the customer information list one by one, and judging whether the current charged limit of the customer exceeds the authorized limit; if yes, automatically carrying out early warning, and executing the operation of limiting the use of financial services for the customer;
if not, classifying according to the customer requirements, and carrying out risk control corresponding to the early warning threshold value under the risk model.
Preferably, when there is a change in the input data of the risk model, retraining the risk model to obtain a retrained risk control model, updating the refitted risk model or the retrained risk model by incremental learning with streaming data; using the risk model as an online model and using the updated refitted risk model and the retrained risk model as backup models; and replacing the online model with one of the backup models when the one of the backup models is superior to the online model.
Preferably, retraining the risk model further comprises: adjusting structural parameters of the risk model; and adjusting the hyper-parameters of the risk control model.
Preferably, the updating the refitted risk control model or the retrained risk control model with streaming data through incremental learning is performed using an FTRL algorithm and an Online Random Forest (Online Random Forest) algorithm.
Preferably, the step of allocating credit lines to the clients in the client information list one by one specifically includes: determining the credit rating of the customer according to the historical order information and the rating of the customer; and allocating credit lines for the clients according to the credit rating and the current operation index of the company.
The invention has the beneficial effects that: the perfect risk main data management of the supply chain management platform ensures that the wind control data dimension is more complete and comprehensive, the information extraction is more efficient, the interference of human factors is avoided, and a solid foundation is laid for risk modeling; the accident risk monitoring system can ensure the timely processing of abnormal conditions; and the quantitative risk model based on big data analysis helps enterprises to fully utilize data assets and predict risks.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic diagram of the establishment process of the present invention.
Detailed Description
As shown in fig. 1, a method for foreign trade supply chain financial risk control comprises the following steps:
establishing a risk model, acquiring various risk data from a supply chain management platform, setting early warning ranges under various risk scenes based on a quantitative risk model of big data analysis, namely risk index data threshold values;
retraining the risk model to obtain a retrained risk model when input data of the risk model changes;
acquiring a customer information list from a supply chain management platform, distributing credit limits for customers in the customer information list one by one, and judging whether the current charged limit of the customer exceeds the authorized limit; if yes, automatically carrying out early warning, and executing the operation of limiting the use of financial services for the customer;
if not, classifying according to the customer requirements, and carrying out risk control corresponding to the early warning threshold value under the risk model.
Retraining the risk model to obtain a retrained risk control model when input data of the risk model changes, updating the refitted risk model or the retrained risk model by incremental learning with streaming data; using the risk model as an online model and using the updated refitted risk model and the retrained risk model as backup models; and replacing the online model with one of the backup models when the one of the backup models is superior to the online model. Retraining the risk model further comprises: adjusting structural parameters of the risk model; and adjusting the hyper-parameters of the risk control model. Updating the refitted risk control model or the retrained risk control model with streaming data via incremental learning is performed using an FTRL algorithm and an online random Forest (Onlinerandom Forest) algorithm. The step of allocating credit line to the clients in the client information list one by one specifically comprises the following steps: determining the credit rating of the customer according to the historical order information and the rating of the customer; and allocating credit lines for the clients according to the credit rating and the current operation index of the company.
Specifically, a corresponding wind control means is adopted, and the wind control means comprises:
when financial service is provided for a client, a client financing standing book is established, financing amount is set for the client, and the use condition of the financing amount is controlled in real time through the financing standing book;
when tax refunding service is provided for a client, paying conditions of a tax bureau are paid attention in real time, the amount of the tax refund payment for the client in a letter call is adjusted in real time, whether the outlet commodity has the letter call and returns the letter normally within one year is paid attention, a tax refund payment standing book is established at the same time, the amount of the tax refund payment is set for the client when the tax refund payment service is provided for part of special clients, and the using condition of the amount is controlled in real time through the tax refund payment standing book.
Specifically, in the trade cooperation process, risks may be encountered in various stages, for example, national policy adjustment, major abnormality of an enterprise itself, abnormal logistics, abnormal product line and product, sudden fluctuation of commodity price, and the like, so that control is very important.
The method comprises the steps of firstly, establishing a risk model, formulating a risk rule, and setting early warning ranges under various scenes, wherein if the price fluctuation exceeds 10%, important concerned commodities need to be rechecked, and the monthly refund amount exceeds 100 million RMB and the like.
And step two, risk clearance, classifying the requirements of the customers, and adopting a corresponding wind control means:
when financial service is provided for a client, business personnel establish a client financing standing book, set financing amount for the client, and control the use condition of the financing amount in real time through the financing standing book. In the service process, in order to prevent goods change and goods leakage and confirm the control of the right of goods, a worker supervises the whole process of loading goods on site, takes pictures and uploads the pictures to a foreign trade supply chain service system, so that the risk is reduced; when tax refunding service is provided for a client, paying conditions of a tax bureau are paid in real time, the amount of the tax refund payment for the client in a letter call is adjusted in real time, whether the outlet commodity has the letter call and returns normally within one year is paid, meanwhile, a tax refund payment platform account is established, when the tax refund payment service is provided for part of special clients, the amount of the tax refund payment is set for the client, and the use condition of the amount is controlled in real time through the tax refund payment platform account. And the assets of goods and customers are supervised in real time offline by installing a camera and PLC equipment. If company asset transfer or other emergencies occur, corresponding measures can be taken in time. When the customer carries out the tax refund payment service, the invoice and the freight bill need to be collected, and partial customers can not bill the freight bill in time due to special conditions, so that a future bill tax refund payment desk account is established, the amount of the future bill tax refund payment is set, and the amount is controlled. In the business process, the situations of customer service misuse, incapability of repayment of customers and the like can occur, the existing fund with fixed use in the account can be frozen, the fund freezing management is carried out, and the special purpose is realized. The wind control personnel pay attention to the policies, the current bureaus, the national conditions and the like issued by the countries, analyze the countries with transaction risks recently, manage the countries with high risks and blacklists, and perform targeted control in the process of export, tax refund payment and financial services of the countries. And (4) setting a ticket collection early warning for the outlet commodity exceeding the preset time, and early warning and follow-up are carried out on the commodity without timely receiving the money or the goods ticket, so that the wind control personnel can track the commodity in real time.
The risk is not derived from traditional risk sources such as client credit risk, trade background authenticity and the like, but is diffused to the upstream and downstream of the supply chain by taking a trade link as a starting point. Therefore, not only the credit level and repayment ability of the customer itself should continue to be of concern, but also the relationship, reputation, credit worthiness, financial statement authenticity, etc. of the customer upstream suppliers, downstream customers. The perfect risk main data management enables the wind control data dimension to be more complete and comprehensive, the information extraction to be more efficient, the interference of human factors is avoided, and a solid foundation is laid for risk modeling; the accident risk monitoring system can ensure the timely processing of abnormal conditions; and the quantitative risk model based on big data analysis helps enterprises to fully utilize data assets and predict risks.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A method of foreign trade supply chain financial risk control comprising the steps of:
establishing a risk model, acquiring various risk data from a supply chain management platform, setting early warning ranges under various risk scenes based on a quantitative risk model of big data analysis, namely risk index data threshold values;
retraining the risk model to obtain a retrained risk model when input data of the risk model changes;
acquiring a customer information list from a supply chain management platform, distributing credit limits for customers in the customer information list one by one, and judging whether the current charged limit of the customer exceeds the authorized limit; if yes, automatically carrying out early warning, and executing the operation of limiting the use of financial services for the customer;
if not, classifying according to the customer requirements, and carrying out risk control corresponding to the early warning threshold value under the risk model.
2. The method of foreign trade supply chain financial risk control according to claim 1, wherein when there is a change in the input data of the risk model, retraining the risk model to obtain a retrained risk control model, updating the refitted risk model or the retrained risk model by incremental learning with streaming data; using the risk model as an online model and using the updated refitted risk model and the retrained risk model as backup models; and replacing the online model with one of the backup models when the one of the backup models is superior to the online model.
3. The method of foreign trade supply chain financial risk control according to claim 1, wherein retraining the risk model further comprises: adjusting structural parameters of the risk model; and adjusting the hyper-parameters of the risk control model.
4. The method of a foreign trade supply chain financial risk control according to claim 1, wherein said updating the refitted risk control model or the retrained risk control model with streaming data through incremental learning is performed using FTRL algorithm and Online Random Forest (Forest) algorithm.
5. The method of claim 1, wherein said step of assigning credit lines to customers in said customer information list one by one comprises: determining the credit rating of the customer according to the historical order information and the rating of the customer; and allocating credit lines for the clients according to the credit rating and the current operation index of the company.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111815207A (en) * | 2020-08-27 | 2020-10-23 | 北京每日优鲜电子商务有限公司 | Risk quantitative evaluation method for supply chain finance |
| CN112102075A (en) * | 2020-11-04 | 2020-12-18 | 南京中科软智信息技术有限公司 | Supply chain finance ecosystem of N + N mode |
| CN112288573A (en) * | 2020-12-25 | 2021-01-29 | 支付宝(杭州)信息技术有限公司 | Method, device and equipment for constructing risk assessment model |
| CN112948749A (en) * | 2021-03-02 | 2021-06-11 | 北京交通大学 | System and method for identifying and predicting risk factors of full-chain logistics |
| CN115760125A (en) * | 2023-01-09 | 2023-03-07 | 中企云链（北京）金融信息服务有限公司 | Production and fusion data risk control method and system based on block chain and storage medium |
| CN117436705A (en) * | 2023-12-11 | 2024-01-23 | 深圳市明心数智科技有限公司 | Trade risk analysis method, system and medium |
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| CN110310206A (en) * | 2019-07-01 | 2019-10-08 | 阿里巴巴集团控股有限公司 | For updating the method and system of risk control model |
| CN110334814A (en) * | 2019-07-01 | 2019-10-15 | 阿里巴巴集团控股有限公司 | For constructing the method and system of risk control model |
| CN110490702A (en) * | 2019-08-09 | 2019-11-22 | 深圳市友创供应链管理有限公司 | Client risk control method and system based on supply chain management platform |
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2019
- 2019-12-31 CN CN201911424829.3A patent/CN111127197A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110310206A (en) * | 2019-07-01 | 2019-10-08 | 阿里巴巴集团控股有限公司 | For updating the method and system of risk control model |
| CN110334814A (en) * | 2019-07-01 | 2019-10-15 | 阿里巴巴集团控股有限公司 | For constructing the method and system of risk control model |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111815207A (en) * | 2020-08-27 | 2020-10-23 | 北京每日优鲜电子商务有限公司 | Risk quantitative evaluation method for supply chain finance |
| CN112102075A (en) * | 2020-11-04 | 2020-12-18 | 南京中科软智信息技术有限公司 | Supply chain finance ecosystem of N + N mode |
| CN112288573A (en) * | 2020-12-25 | 2021-01-29 | 支付宝(杭州)信息技术有限公司 | Method, device and equipment for constructing risk assessment model |
| CN112948749A (en) * | 2021-03-02 | 2021-06-11 | 北京交通大学 | System and method for identifying and predicting risk factors of full-chain logistics |
| CN115760125A (en) * | 2023-01-09 | 2023-03-07 | 中企云链（北京）金融信息服务有限公司 | Production and fusion data risk control method and system based on block chain and storage medium |
| CN117436705A (en) * | 2023-12-11 | 2024-01-23 | 深圳市明心数智科技有限公司 | Trade risk analysis method, system and medium |
| CN117436705B (en) * | 2023-12-11 | 2024-04-19 | 深圳市明心数智科技有限公司 | Trade risk analysis method, system and medium |
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