An import and export enterprise risk assessment method based on machine learning

Abstract

The application discloses an import and export enterprise risk assessment method based on machine learning, comprising the following steps: constructing an enterprise risk profile database according to historical monitoring data, and counting the total number of each matter; generating the time weight and risk weight of each type of matter; calculating the risk value of each risk matter of the overall matter of the enterprise; dividing the risk level of the enterprise according to the corresponding risk value; calculating the membership degree of the enterprise risk in each risk level; and judging whether the enterprise is in the qualified range according to the membership degree matrix and feeding back. The application realizes risk analysis of the target enterprise through historical monitoring data, an enterprise risk profile database, a time risk double attention module, an enterprise risk assessment module, a risk level division module, a first-level fuzzy comprehensive evaluation model and a feedback module, so that whether the enterprise has risks can be judged according to the risk value of the target enterprise.

Description

Technical Field

[0001] The present invention relates to the field of computers and machine learning technologies, and in particular to a risk assessment method and system for import and export enterprises based on machine learning. Background Art

[0002] With the development of globalization, market exchanges between countries around the world are becoming increasingly close, and product exports have become an important means for countries to develop their economies. Today, China's export market continues to expand, and it has become one of the world's largest exporters. With the expansion of the international market, important cooperation between countries has become a mutual economic dependence.

[0003] Currently, product exports are a crucial source of economic growth for a nation. With the continued advancement of globalization, the dependence of product exports on national economies will only grow. Export and import trade has become the most common form of economic interaction between nations, but the laws, regulations, and taxes involved are becoming increasingly complex. If an import or export company is investigated by public security or industrial and commercial administration authorities for engaging in certain illegal activities, or experiences high-risk situations such as funding shortfalls or bond defaults, then the company may be considered a risky enterprise. Other companies that participate in the risky enterprise's operations or have profited from it may also suffer financial losses from the risky enterprise's funding shortfalls, or risk personnel being suspected of crimes. Therefore, the choice of corporate channels for importing and exporting goods is crucial, and determining the riskiness of import and export companies has become a major challenge. Current technology has yet to provide an effective solution to these issues.

[0004] Machine learning technology can simulate human thinking and utilize self-learning to achieve data analysis, decision-making, optimization, and prediction. This learning capability is applied in risk assessment, playing a particularly important role in quantifying risk. Fuzzy comprehensive evaluation is a commonly used machine learning method. Fuzzy comprehensive evaluation models are capable of handling complex multi-metric problems. Practical decision-making often involves considering multiple metrics, which may be correlated and mutually influential. Traditional evaluation methods often only consider a few metrics, making it difficult to fully grasp the essence of the problem. Fuzzy comprehensive evaluation models, on the other hand, integrate information from different metrics, comprehensively considering the weights and correlations of each metric, thereby enabling more comprehensive decision analysis and evaluation.

[0005] Due to the large intervals between enterprise data updates, the data used in the assessment model is not necessarily the enterprise's current real-time data, and there is a certain degree of ambiguity. For the ambiguity problem of enterprise risk assessment, it is very difficult to accurately define the dynamic characteristics of customs risk assessment for import and export enterprises using traditional mathematical theories. This patent combines fuzzy mathematics to construct a risk assessment model, which can form a simpler and more efficient import and export enterprise risk assessment method that does not rely on internal enterprise data and customs historical behavior data. While reducing the difficulty of obtaining indicators, it ensures the universality of the assessment for all enterprises. Summary of the Invention

[0006] In light of this, the present invention aims to provide a machine learning-based risk assessment method for import and export enterprises. By integrating historical monitoring data, an enterprise risk profile database, a time-risk dual-attention module, an enterprise risk assessment module, a risk classification module, a first-level fuzzy comprehensive evaluation model, and a feedback module, the method analyzes the risk of a target enterprise, thereby determining the riskiness of the target enterprise based on its risk value.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] The present invention provides a risk assessment method for import and export enterprises based on machine learning, comprising the following steps:

[0009] Step S1: Build an enterprise risk profile database based on historical monitoring data and count the total number of each item;

[0010] Step S2: Input the enterprise information to be assessed from the enterprise risk profile database into the time-risk dual attention module to generate the time weight and risk weight for each type of matter;

[0011] Step S3: Construct an enterprise risk value assessment module to calculate the risk value of each risk item for the overall enterprise matters;

[0012] Step S4: Construct a risk level classification module to classify the risk level of the enterprise according to the corresponding risk value;

[0013] Step S5: construct a first-level fuzzy comprehensive evaluation model to generate a membership matrix and calculate the membership of the enterprise risk in each risk level;

[0014] Step S6: Determine whether the enterprise is within the qualified range based on the membership matrix and provide feedback to relevant personnel.

[0015] Furthermore, step S1 includes the following sub-steps:

[0016] Step S101: Pre-process the historical monitoring data and classify the various matters of the enterprise. The types of matters classified include but are not limited to the list of untrustworthy entities, business anomalies, administrative penalties, and credit commitments;

[0017] Step S102: Count the number of each type of items, and count the number of each item for each enterprise;

[0018] Step S103: storing the above four types of items of a single enterprise information into the same set of data, and storing all enterprise information into the enterprise risk profile database in the same way;

[0019] Furthermore, step S2 specifically includes the following sub-steps:

[0020] Step S201: constructing a temporal risk dual attention module;

[0021] Step S202: Calculate the attenuation coefficient of the data input into the time-risk dual attention module based on historical information;

[0022] Step S203: Design a time weight algorithm based on the attenuation coefficient to calculate the time weight of each event; the expression of the time weight algorithm is:

[0023]

[0024] in, represents the time weight of the i-th event, T represents the meaning of time, k is the attenuation coefficient of the time weight, Y i is the number of years that have passed since the occurrence of the i-th event;

[0025] Step S204: Design a risk weight algorithm to calculate the data input into the risk attention module to obtain the individual weight of each risk item.

[0026] Furthermore, the risk weight algorithm described in step S204 calculates the data input into the risk attention module to obtain the individual weights of each risk item. The weight algorithm expressions of the three risk items are respectively:

[0027]

[0028]

[0029]

[0030] Among them, W S represents the weight of the list of dishonest subjects, W J Indicates the weight of abnormal operation, W X represents the weight of administrative punishment, N S N is the number of items on the list of all corporate dishonest entities. J N is the number of abnormal business operations of all enterprises. Xis the number of administrative penalties for all enterprises, R is the total number of risk items, r i is the number of risk items of category i;

[0031] Among them, AC( ) is the product addition operation of permutation and combination, and the specific operation method is as follows:

[0032] AC(A, B, C)=A×B+B×C+A×C+A×B×C

[0033] Among them, A, B, and C are arbitrary real numbers.

[0034] Furthermore, the step S3 specifically includes the following sub-steps:

[0035] Step S301: Time weight of each item and the total number of credit commitments N of the enterprises to be assessed Cr Bonus units input into the Enterprise Risk Assessment Module;

[0036] Step S302: construct a positive event algorithm in the bonus unit to calculate the total bonus value of the enterprise; the expression of the positive event algorithm is:

[0037]

[0038] in, is the total bonus value of the enterprise, N Cr is the total number of credit commitments of the enterprise, Y i is the number of years that have passed since the occurrence of the i-th event;

[0039] Step S303: Time weight of each item , the number of negative issues N of the enterprise to be evaluated S 、N J 、N X And the weight of each negative item W S 、W J 、W X , input the deduction unit;

[0040] Step S304: constructing a negative event algorithm in the deduction unit to calculate the deduction value of the enterprise;

[0041] Furthermore, in step S304, in the calculation of the enterprise's deduction value by constructing a negative event algorithm in the deduction unit, the expression of the negative event algorithm is:

[0042]

[0043]

[0044]

[0045] Among them, N S N is the total number of the enterprise’s defaulter list. J is the total number of abnormal operations of the enterprise, N X is the total number of administrative penalties imposed on the enterprise, is the time weight of the i-th event, PTS S Points deducted for items on the list of dishonest subjects, PTS J Points are deducted for abnormal business operations. X Points deducted for administrative penalties.

[0046] Further, step S4 specifically includes:

[0047] The existing risky enterprises are divided into four levels: low risk, medium risk, medium-high risk and high risk;

[0048] The degree of risk is divided according to the risk value: enterprises with a risk value less than 80 are high-risk enterprises, enterprises with a risk value less than 90 but greater than or equal to 80 are medium-high risk enterprises, enterprises with a risk value less than 97 but greater than or equal to 90 are medium-risk enterprises, and enterprises with a risk value greater than or equal to 97 are low-risk enterprises.

[0049] The median risk value of each risk level is calculated based on the score threshold of each risk level, and the results are ranked from low risk to high risk on a percentage basis: the median risk value of low risk is (100+97) / 2, which is 98.5; the median risk value of medium risk is (97+90) / 2, which is 93.5; the median risk value of medium-high risk is (90+80) / 2, which is 85; and the median risk value of high risk is (80+0) / 2, which is 40.

[0050] Furthermore, step S5 specifically includes the following sub-steps:

[0051] Step S501: setting a comment set according to each risk level;

[0052] Step S502: Calculate the evaluation criteria of each comment set by integrating the bonus values ​​of the bonus units; the calculation formula is as follows:

[0053] std m =mid m +

[0054] Among them, std m is the evaluation criteria for the mth review set, mid m is the median risk value of the mth review set, The total bonus points for the enterprise;

[0055] Step S503: designing a trapezoidal distribution membership function to calculate the fuzzy comprehensive evaluation matrix;

[0056] Step S504: combining the design weight matrix with the fuzzy comprehensive evaluation matrix to generate a membership matrix;

[0057] Step S505: Generate the membership degree of enterprise risk in each risk level from the membership matrix;

[0058] Furthermore, in step S503, a trapezoidal distribution membership function is designed to calculate the fuzzy comprehensive evaluation matrix. The specific formula of the trapezoidal distribution membership function is as follows:

[0059]

[0060]

[0061]

[0062]

[0063] a m =100- std m

[0064] Among them, f ij is the initial membership of the i-th negative item in the j-th review set, PTS i is the deduction score for the i-th negative item, i.e., the deduction score PTS for the item on the list of dishonest subjects calculated in step S304 S , deduction points for abnormal business operations PTS J and PTS points deduction for administrative penalties S , a m is the membership value of the mth comment set, std m is the evaluation criteria for the mth review set;

[0065] Its fuzzy comprehensive evaluation matrix is:

[0066]

[0067] The step S504 designs a weight matrix and combines it with the fuzzy comprehensive evaluation matrix to generate a membership matrix. The membership matrix calculation formula is as follows:

[0068] B=A×F

[0069] Among them, B is the membership matrix, which has one row and four columns; F is the fuzzy comprehensive evaluation matrix, which has three rows and four columns; A is the weight matrix, which has one row and three columns. Its specific composition is as follows:

[0070]

[0071] The step S505 generates the membership of the enterprise risk in each risk level from the membership matrix, and the membership matrix is ​​as follows:

[0072]

[0073] Among them, b1 is the degree of membership when the enterprise risk level is low risk, b2 is the degree of membership when the enterprise risk level is medium risk, b3 is the degree of membership when the enterprise risk level is medium-high risk, and b4 is the degree of membership when the enterprise risk level is high risk.

[0074] Furthermore, step S6 determines whether the enterprise is within the qualified range based on the membership matrix and provides feedback to relevant personnel, which specifically includes the following sub-steps:

[0075] Step S601: inputting the membership matrix of the enterprise to be evaluated into the early warning unit in the feedback module;

[0076] Step S602: The early warning unit determines whether to issue an early warning based on the membership matrix. If the enterprise risk level is high, the process proceeds to S603; otherwise, the process proceeds to S604.

[0077] Step S603: Feedback the enterprise's information to relevant processing personnel and conduct a careful review of the enterprise;

[0078] Step S604: Display the risk affiliation of the enterprise and the total number of each item.

[0079] In step S602, the early warning unit determines whether to issue an early warning based on the risk level. If the high-risk membership of the enterprise is greater than 50% (ie 0.5), the process proceeds to S603; otherwise, the process proceeds to S604.

[0080] The beneficial effects of the present invention include:

[0081] (1) The first-level fuzzy comprehensive evaluation model constructed by the present invention adopts a trapezoidal distribution membership function. It takes the customs data of import and export enterprises as the starting point, collects the enterprise information that is open and more easily accessible to import and export enterprises, and constructs a customs risk assessment model. It forms a simpler and more efficient import and export enterprise risk assessment method that does not rely on the internal data of the enterprises and the historical customs behavior data. While reducing the difficulty of obtaining indicators, it ensures the universality of the assessment for all enterprises.

[0082] (2) The present invention assigns different weights to each negative event by designing a risk weighting algorithm for each negative event, thereby increasing the differences between each negative event and weighing the risk level brought to the enterprise by each negative event, thereby improving the accuracy of enterprise risk assessment and providing detailed enterprise data to relevant departments;

[0083] (3) This invention accurately predicts the risk level of a target enterprise by calculating the enterprise risk value using real data. The calculation method used is derived from the current basis for judging the cooperation conditions of state-owned enterprises and has a certain degree of updating and iteration mechanism, eliminating the errors caused by manual input and making the judgment basis more scientific and accurate. In addition, it can reduce labor costs and increase enterprise profits.

[0084] (4) The present invention designs a time weight algorithm to evaluate the weights of events occurring in each year, wherein the attenuation coefficient k of the time weight is added, and its value can be dynamically adjusted according to the different institutional conditions of each year to balance the impact of each event on the enterprise each year. The risk assessment of the enterprise has a certain degree of compensability and conducts risk assessment from a global perspective. It is applicable to most enterprises and improves the universality of the model.

[0085] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description and the preceding claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0087] Figure 1 It is a schematic diagram of the process of the present invention;

[0088] Figure 2 It is a system structure diagram of the present invention;

[0089] Figure 3 This is the data storage format of the enterprise Q to be evaluated in an embodiment of the present invention.

[0090] Figure 4 This is a structural diagram of the temporal risk dual attention module of the present invention;

[0091] Figure 5 This is a structural diagram of the enterprise risk value assessment module of the present invention. DETAILED DESCRIPTION

[0092] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments are only for illustrating the present invention, and are not intended to limit the scope of protection of the present invention.

[0093] The schematic flow diagram of the present invention is as follows Figure 1 As shown, the system structure diagram of the present invention is as follows Figure 2As shown, this embodiment provides an import and export enterprise risk assessment method based on machine learning, which specifically includes the following steps:

[0094] Step S1: Build an enterprise risk profile database based on historical monitoring data and count the total number of each item;

[0095] In this embodiment, step S1 specifically includes the following steps:

[0096] Step S101: Pre-process the historical monitoring data and classify the enterprise's matters into the list of dishonest entities, operational anomalies, administrative penalties, and credit commitments;

[0097] Step S102: Count the number of each type of items, and count the number of each item for each enterprise;

[0098] Step S103: storing the above four items of information of a single enterprise into the same set of data, and storing all enterprise information into the enterprise risk profile database in the same manner;

[0099] Among them, step S102 counts the total number of each item and counts the independent number of each item for each enterprise. The number of each item is based on the official data of "Credit China".

[0100] In this embodiment, step S102 counts the total number of each item and the independent number of each item for each enterprise. The number of each item is based on the official data of Credit China.

[0101] As of March 14, 2021, a total of 442,000 items of information on the list of untrustworthy entities, 8.812 million items of information on the list of abnormal operations, and 1.047 million administrative penalties have been reported across China;

[0102] In this embodiment, step S103 stores the above four items of information of a single enterprise into the same set of data, and stores all enterprise information into the enterprise risk profile database in the same way. The historical monitoring data stored in the enterprise risk profile database is stored in the form of a data table. Taking the Q information of the enterprise to be evaluated in this embodiment as an example, its storage format is as follows: Figure 3 As shown;

[0103] The horizontal axis of the table represents the name of the enterprise's event category, the vertical axis of the table represents the year in which the event occurred, and the intersection of the horizontal and vertical axes of the table represents the number of events of the enterprise to be evaluated.

[0104] S2: Input the enterprise information to be assessed from the enterprise risk profile database into the time-risk dual attention module to generate the time weight and risk weight for each type of matter;

[0105] In this embodiment, step S2 specifically includes the following steps:

[0106] Step S201: constructing a temporal risk dual attention module;

[0107] Step S202: Calculate the attenuation coefficient of the data input into the time attention module based on historical information;

[0108] Step S203: designing a time weight algorithm based on the attenuation coefficient to calculate the time weight of each item;

[0109] Step S204: Design a risk weight algorithm to calculate the data input into the risk attention module to generate a separate weight for each risk item.

[0110] Among them, the time-risk dual attention module consists of a time attention unit and a risk attention unit, whose functions are to calculate the time weight and the weight of various risk items respectively. Its structure diagram is as follows Figure 4 As shown;

[0111] In this embodiment, step S202 calculates the attenuation coefficient of the data input into the time attention module based on historical information. The attenuation coefficient needs to be calculated based on the statute of limitations for prosecution of corporate violations of laws and regulations.

[0112] Among them, as of May 17, 2023, because relevant laws of my country stipulate that the statute of limitations for prosecution of corporate violations is 20 years, the attenuation coefficient k can be calculated based on the severity of the average annual decrease by a certain coefficient: k = 1 / 20, that is, the attenuation coefficient k in the time weighted algorithm is 0.05;

[0113] In this embodiment, in step S203, a time weighting algorithm is designed according to the attenuation coefficient to calculate the time weight of each event. The expression of the time weighting algorithm is:

[0114]

[0115] in, represents the time weight of the i-th event, T represents the meaning of time, k is the attenuation coefficient of the time weight, Y i is the number of years that have passed since the occurrence of the i-th event;

[0116] In this embodiment, substituting k=0.05 into the above formula, the expression of the time weight algorithm can be obtained as follows:

[0117]

[0118] In this embodiment, step S204 designs a risk weight algorithm to calculate the data input into the risk attention module to generate the individual weights of each risk item. The weight algorithm expressions of the three risk items are respectively:

[0119]

[0120]

[0121]

[0122] Among them, W S represents the weight of the list of dishonest subjects, W J Indicates the weight of abnormal operation, W X represents the weight of administrative punishment, N S N is the number of items on the list of all corporate dishonest entities. J N is the number of abnormal business operations of all enterprises. X is the number of administrative penalties for all enterprises, R is the total number of risk items, r i is the number of risk items of category i;

[0123] Among them, AC () is the product addition operation of permutation and combination, and the specific operation method is as follows:

[0124] AC(A, B, C)=A×B+B×C+A×C+A×B×C

[0125] Among them, A, B, and C are arbitrary real numbers.

[0126] In this embodiment, the number of items on the list of all corporate dishonest entities is N S The number of abnormal business operations of all enterprises is N. J The number of administrative penalties for all enterprises is N. X The weighting algorithm expressions of the three risk items can be obtained by substituting the above three data into:

[0127]

[0128]

[0129]

[0130] The weight W of the severity of the items in the list of dishonest subjects is obtained. S =0.679, the weight of the severity of abnormal business events W J =0.034, the weight of the severity of administrative penalties W X =0.287.

[0131] Step S3: Construct an enterprise risk value assessment module to calculate the risk value of each risk item for the overall enterprise matters;

[0132] In this embodiment, step S3 specifically includes the following steps:

[0133] Step S301: Time weight of each item and the total number of credit commitments N of the enterprises to be assessed Cr Bonus units input into the Enterprise Risk Assessment Module;

[0134] Step S302: constructing a positive event algorithm in the bonus unit to calculate the total bonus value of the enterprise;

[0135] Step S303: Time weight of each item , the number of negative issues N of the enterprise to be evaluated S 、N J 、N X And the weight of each negative item W S 、W J 、W X , input the deduction unit;

[0136] Step S304: constructing a negative event algorithm in the deduction unit to calculate the deduction value of the enterprise;

[0137] Among them, the enterprise risk value assessment module of the import and export enterprise risk assessment method and system based on machine learning of the present invention is shown in FIG Figure 5 As shown, the module consists of a point-adding unit and a point-deducting unit, which calculate the total point-adding value and the total point-deducting value respectively;

[0138] In this embodiment, step S302 constructs a positive event algorithm in the bonus unit to calculate the total bonus value of the enterprise. The expression of the positive event algorithm is:

[0139]

[0140] in, is the total bonus value of the enterprise, N Cr is the total number of credit commitments of the enterprise, Y i is the number of years that have passed since the occurrence of the i-th event;

[0141] In this embodiment, if Figure 3 As shown, the total number of corporate credit commitments N Cr =2, the years are 2022 and 2019 respectively, and the years since now are 1 year and 4 years respectively. Substituting the above data, the expression of the positive event algorithm is:

[0142] That is, the total bonus value of the enterprise can be obtained by calculation is 1.75;

[0143] In this embodiment, step S304 constructs a negative event algorithm in the deduction unit to calculate the deduction value of the enterprise. The expression of the negative event algorithm is:

[0144]

[0145]

[0146]

[0147] Among them, N S N is the total number of the enterprise’s defaulter list. J is the total number of abnormal operations of the enterprise, N X is the total number of administrative penalties imposed on the enterprise, is the time weight of the i-th event, PTS S Points deducted for items on the list of dishonest subjects, PTS J Points are deducted for abnormal business operations. X Points deducted for administrative penalties.

[0148] In this embodiment, if Figure 3 As shown, the total number of the defaulter list of enterprise Q is N S is 4, the years are 2022, 2022, 2020 and 2019 respectively, and the years from now are 1 year, 1 year, 3 years and 4 years respectively; the total number of abnormal business operations N J is 4, the years are 2023, 2021, 2018 and 2018 respectively, and the years since then are 0, 2, 5 and 5 years respectively; the total number of administrative penalties for enterprise Q is N X The number is 3, the years are 2020, 2020 and 2018 respectively, and the years since then are 3 years, 3 years and 5 years respectively. Substituting the above data, the deduction points for each negative matter are:

[0149]

[0150]

[0151]

[0152] PTS points deduction for items on the list of dishonest subjects S The PTS score is 3.55, and the deduction score for abnormal business operations is J The PTS score is 3.4, and the administrative penalty score is deducted. X is 2.45.

[0153] Step S4: Construct a risk level classification module to classify the risk level of the enterprise according to the corresponding risk value;

[0154] In this embodiment, step S4 specifically includes the following steps:

[0155] Step S401: query the latest "White Paper on Risk Index of Chinese Listed Companies" through the risk level classification module;

[0156] Step S402: Obtain the boundary risk value for risk level classification in the latest system from the "White Paper on Risk Index of Chinese Listed Companies";

[0157] Step S403: Calculate the median risk value of each risk level according to the score threshold of each risk level;

[0158] In this embodiment, step S401 is to query the latest "China Listed Company Risk Index White Paper" by the risk level classification module, wherein the "China Listed Company Risk Index White Paper" contains the most authoritative evaluation indicators for enterprise risk assessment;

[0159] In this embodiment, step S402 obtains the boundary risk value for dividing risk levels from the "White Paper on Risk Index of Chinese Listed Companies". The "White Paper on Risk Index of Chinese Listed Companies" divides existing risky enterprises into four levels, namely low risk, medium risk, medium-high risk and high risk;

[0160] Among them, as of now, the "White Paper on Risk Index of Chinese Listed Companies" divides the degree of risk according to the risk value: a risk value of less than 80 points is a high-risk enterprise, a risk value of less than 90 points but greater than or equal to 80 points is a medium-high risk enterprise, a risk value of less than 97 points but greater than or equal to 90 points is a medium-risk enterprise, and a risk value greater than or equal to 97 points is a low-risk enterprise;

[0161] In this embodiment, step S403 calculates the median risk value of each risk level according to the score threshold of each risk level, calculates it on a percentage basis, and arranges the results from low risk to high risk as follows: the median risk value of low risk is (100+97) / 2, which is 98.5; the median risk value of medium risk is (97+90) / 2, which is 93.5; the median risk value of medium-high risk is (90+80) / 2, which is 85; and the median risk value of high risk is (80+0) / 2, which is 40.

[0162] Step S5: construct a first-level fuzzy comprehensive evaluation model to generate a membership matrix and calculate the membership of the enterprise risk in each risk level;

[0163] In this embodiment, step S5 specifically includes the following steps:

[0164] Step S501: setting a comment set according to each risk level;

[0165] Step S502: Calculate the evaluation criteria of each comment set by integrating the bonus values ​​of the bonus units;

[0166] Step S503: designing a trapezoidal distribution membership function to calculate the fuzzy comprehensive evaluation matrix;

[0167] Step S504: combining the design weight matrix with the fuzzy comprehensive evaluation matrix to generate a membership matrix;

[0168] Step S505: Generate the membership degree of enterprise risk in each risk level from the membership matrix;

[0169] In this embodiment, step S501 sets a comment set according to each risk level. In the enterprise risk assessment, the comment set m is 4, which are low risk, medium risk, medium-high risk and high risk respectively;

[0170] In this embodiment, step S502 integrates the bonus values ​​of the bonus units to calculate the evaluation criteria of each comment set, and the calculation formula is as follows:

[0171] std m =mid m +

[0172] Among them, std m is the evaluation criteria for the mth review set, mid m is the median risk value of the mth review set, The total bonus points for the enterprise;

[0173] In this embodiment, the median risk values ​​of the review sets are 98.5, 93.5, 85, and 40, respectively. The std of the evaluation criteria of each review set calculated by the above formula is 100.25, 95.25, 86.75, and 41.75, respectively;

[0174] Step S503: Design a trapezoidal distribution membership function to calculate the fuzzy comprehensive evaluation matrix. The specific formula of the trapezoidal distribution membership function is as follows:

[0175]

[0176]

[0177]

[0178]

[0179] a m =100- std m

[0180] Among them, f ij is the initial membership of the i-th negative item in the j-th review set, PTS iis the deduction score for the i-th negative item, i.e., the deduction score PTS for the item on the list of dishonest subjects calculated in step S304 S , deduction points for abnormal business operations PTS J and PTS points deduction for administrative penalties X , a m is the membership value of the mth comment set, std m is the evaluation criteria for the mth review set;

[0181] In this embodiment, the membership values ​​a1, a2, a3, and a4 of the comment set are calculated to be -0.25, 4.75, 13.25, and 58.25, respectively. The specific formula of the trapezoidal distribution membership function can be updated as follows:

[0182]

[0183]

[0184]

[0185]

[0186] In this embodiment, step S503 designs a trapezoidal distribution membership function to calculate a fuzzy comprehensive evaluation matrix, and the fuzzy comprehensive evaluation matrix is:

[0187]

[0188] In this embodiment, the fuzzy comprehensive evaluation matrix has three rows and four columns, and the specific format is as follows:

[0189]

[0190] In this embodiment, the fuzzy comprehensive evaluation matrix can be obtained by calculating the above formula:

[0191]

[0192] Step S504 designs a weight matrix and combines it with the fuzzy comprehensive evaluation matrix to generate a membership matrix. The membership matrix calculation formula is as follows:

[0193] B=A×F

[0194] Among them, B is the membership matrix, which has one row and four columns; F is the fuzzy comprehensive evaluation matrix, which has three rows and four columns; A is the weight matrix, which has one row and three columns. Its specific composition is as follows:

[0195]

[0196] In this embodiment, the weight matrix A is as follows:

[0197]

[0198] Step S505 generates the membership of the enterprise risk in each risk level from the membership matrix, and the membership matrix is ​​as follows:

[0199]

[0200] Among them, b1 is the membership degree of the enterprise risk level being low risk, b2 is the membership degree of the enterprise risk level being medium risk, b3 is the membership degree of the enterprise risk level being medium-high risk, and b4 is the membership degree of the enterprise risk level being high risk;

[0201] In this embodiment, the weight matrix B obtained through the above calculation is as follows:

[0202]

[0203] Among them, the enterprise's risk level is low risk and the membership degree is 0.24676, and the enterprise's risk level is medium risk and the membership degree is 0.75324.

[0204] S6: Determine whether the enterprise is within the qualified range based on the membership matrix and provide feedback to relevant personnel;

[0205] In this embodiment, step S6 specifically includes the following steps:

[0206] S601: Input the membership matrix of the enterprise to be evaluated into the early warning unit in the feedback module;

[0207] S602: The early warning unit determines whether to issue an early warning based on the membership matrix. If the enterprise risk level is high, the process proceeds to S603; otherwise, the process proceeds to S604.

[0208] S603: Feedback the enterprise's information to the relevant processing personnel and conduct a careful review of the enterprise;

[0209] S604: Display the risk affiliation of each enterprise and the total number of each matter.

[0210] In this embodiment, in step S602, the early warning unit determines whether to issue an early warning based on the risk level. If the high-risk membership of the enterprise is greater than 50% (i.e., 0.5), the process proceeds to S603. Otherwise, the process proceeds to S604. Customs will strictly inspect the import and export products of high-risk enterprises, so it is necessary to issue early warnings for the assessed high-risk enterprise information.

[0211] Among them, the high-risk membership of enterprise Q is 0, which is less than 50%, so it will not enter S603;

[0212] In this embodiment, in step S604, the display unit displays the enterprise's risk level and the total details of each event. The display unit needs to display the enterprise's risk level and the total details of the four events: the list of defaulting entities, abnormal operations, administrative penalties, and credit commitments.

[0213] Among them, the output of the display unit is: the risk level of enterprise Q is low risk, the membership degree is 0.24676, the risk level is medium risk, the membership degree is 0.75324, the total number of untrustworthy entities on the list is 4, which occurred 2 times in 2022, 1 time in 2020, and 1 time in 2019 respectively; the total number of enterprise business abnormalities is 4, which occurred 1 time in 2023, 1 time in 2021, and 2 times in 2018 respectively; the total number of administrative penalties is 3, which occurred 2 times in 2020 and 1 time in 2018 respectively; the total number of enterprise credit commitments is 2, which occurred 1 time in 2022 and 1 time in 2019 respectively.

[0214] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0215] Those skilled in the art will understand that all or part of the steps of the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0216] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0217] The aforementioned storage media may be a read-only memory, a magnetic disk, or an optical disk. Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. Throughout this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0218] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that can be executed by one or more processors.

[0219] Further, the methods can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. 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 solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A risk assessment method for import and export enterprises based on machine learning, characterized by: The method comprises the following steps: Step S1: Build an enterprise risk profile database based on historical monitoring data and count the total number of each item; Step S2: Input the enterprise information to be assessed from the enterprise risk profile database into the time-risk dual attention module to generate the time weight and risk weight for each type of matter; Step S2 specifically includes the following sub-steps: Step S201: constructing a time-risk dual attention module, which includes a time attention module and a risk attention module; Step S202: Calculate the attenuation coefficient of the data input into the time attention module based on historical information; Step S203: Design a time weight algorithm based on the attenuation coefficient to calculate the time weight of each event; the expression of the time weight algorithm is: in, represents the time weight of the i-th event, T represents the meaning of time, k is the attenuation coefficient of the time weight, Y i is the number of years that have passed since the occurrence of the i-th event; Step S204: Design a risk weight algorithm to calculate the data input into the risk attention module to obtain the individual weight of each risk item; Step S3: Constructing an enterprise risk value assessment module to calculate the risk value of each risk item for the enterprise's overall matters; Step S3 specifically includes the following sub-steps: Step S301: Time weight of each item and the total number of credit commitments N of the enterprises to be assessed Cr Bonus units input into the Enterprise Risk Assessment Module; Step S302: construct a positive event algorithm in the bonus unit to calculate the total bonus value of the enterprise; the expression of the positive event algorithm is: in, is the total bonus value of the enterprise, N Cr is the total number of credit commitments of the enterprise, Y i is the number of years that have passed since the occurrence of the i-th event; Step S303: Time weight of each item , the number of negative issues N of the enterprise to be evaluated S 、N J 、N X And the weight of each negative item W S 、W J 、W X , input the deduction unit; Step S304: constructing a negative event algorithm in the deduction unit to calculate the deduction value of the enterprise; Step S4: Construct a risk level classification module to classify the risk level of the enterprise according to the corresponding risk value; Step S5: Constructing a first-level fuzzy comprehensive evaluation model to generate a membership matrix and calculating the membership of the enterprise risk in each risk level; Step S5 specifically includes the following sub-steps: Step S501: setting a comment set according to each risk level; Step S502: Calculate the evaluation criteria of each comment set by integrating the bonus values ​​of the bonus units; the calculation formula is as follows: standard m =mid m + Among them, std m is the evaluation criteria for the mth review set, mid m is the median risk value of the mth review set, The total bonus points for the enterprise; Step S503: designing a trapezoidal distribution membership function to calculate the fuzzy comprehensive evaluation matrix; Step S504: combining the design weight matrix with the fuzzy comprehensive evaluation matrix to generate a membership matrix; Step S505: generating the membership degree of the enterprise risk in each risk level from the membership matrix; Step S6: Determine whether the enterprise is within the qualified range based on the membership matrix and provide feedback to relevant personnel, which specifically includes the following sub-steps: Step S601: inputting the membership matrix of the enterprise to be evaluated into the early warning unit in the feedback module; Step S602: The early warning unit determines whether to issue an early warning based on the membership matrix. If the enterprise risk level is high, the process proceeds to S603; otherwise, the process proceeds to S604. Step S603: Feedback the enterprise's information to relevant processing personnel and conduct a careful review of the enterprise; Step S604: Display the details of each risk affiliation of the enterprise and the total number of each item; In step S602, the early warning unit determines whether to issue an early warning based on the risk level. If the high-risk membership of the enterprise is greater than fifty percent, that is, 0.5, the process proceeds to S603; otherwise, the process proceeds to S604.

2. The method for risk assessment of import and export enterprises based on machine learning according to claim 1, characterized in that: The step S1 includes the following sub-steps: Step S101: Pre-process the historical monitoring data and classify the various matters of the enterprise. The types of matters classified include the list of untrustworthy entities, business anomalies, administrative penalties, and credit commitments; Step S102: Count the number of each type of items, and count the number of each item for each enterprise; Step S103: The quantity of the four categories of items of a single enterprise information is stored in the same set of data, and all enterprise information is stored in the enterprise risk profile database in the same way.

3. The method for risk assessment of import and export enterprises based on machine learning according to claim 1, characterized in that: The designed risk weight algorithm described in step S204 calculates the data input into the risk attention module to obtain the individual weights of each risk item. The weight algorithm expressions of the three risk items are respectively: Among them, W S represents the weight of the list of dishonest subjects, W J Indicates the weight of abnormal operation, W X represents the weight of administrative punishment, N S N is the number of items on the list of all corporate dishonest entities. J N is the number of abnormal business operations of all enterprises. X is the number of administrative penalties for all enterprises, R is the total number of risk items, r i is the number of risk items of category i; Among them, AC( ) is the product addition operation of permutation and combination, and the specific operation method is as follows: AC(A, B, C)=A×B+B×C+A×C+A×B×C Among them, A, B, and C are arbitrary real numbers.

4. The method for risk assessment of import and export enterprises based on machine learning according to claim 1, characterized in that: In step S304, the negative event algorithm is constructed in the deduction unit to calculate the deduction value of the enterprise. The expression of the negative event algorithm is: Among them, N S N is the total number of the enterprise’s defaulter list. J is the total number of abnormal operations of the enterprise, N X is the total number of administrative penalties imposed on the enterprise, is the time weight of the i-th event, PTS S Points deducted for items on the list of dishonest subjects, PTS J Points are deducted for abnormal business operations. X Points deducted for administrative penalties.

5. The method for risk assessment of import and export enterprises based on machine learning according to claim 1, characterized in that: Step S4 specifically includes: The existing risky enterprises are divided into four levels: low risk, medium risk, medium-high risk and high risk; The degree of risk is divided into the following categories: enterprises with a risk value less than 80 are classified as high-risk enterprises; enterprises with a risk value less than 90 but greater than or equal to 80 are classified as medium-high-risk enterprises; enterprises with a risk value less than 97 but greater than or equal to 90 are classified as medium-risk enterprises; and enterprises with a risk value greater than or equal to 97 are classified as low-risk enterprises. The median risk value of each risk level is calculated based on the score threshold of each risk level, and the results are ranked from low risk to high risk on a percentage basis: the median risk value of low risk is (100+97) / 2, which is 98.5; the median risk value of medium risk is (97+90) / 2, which is 93.5; the median risk value of medium-high risk is (90+80) / 2, which is 85; and the median risk value of high risk is (80+0) / 2, which is 40.

6. The method for risk assessment of import and export enterprises based on machine learning according to claim 1, characterized in that: The step S503 designs a trapezoidal distribution membership function to calculate the fuzzy comprehensive evaluation matrix. The specific formula of the trapezoidal distribution membership function is as follows: a m =100- std m Among them, f ij is the initial membership of the i-th negative item in the j-th review set, PTS i is the deduction score for the i-th negative item, i.e., the deduction score PTS for the item on the list of dishonest subjects calculated in step S304 S , deduction points for abnormal business operations PTS J and PTS points deduction for administrative penalties S , a m is the membership value of the mth comment set, std m is the evaluation criteria for the mth review set; Its fuzzy comprehensive evaluation matrix is: The step S504 designs a weight matrix and combines it with the fuzzy comprehensive evaluation matrix to generate a membership matrix. The membership matrix calculation formula is as follows: B=A×F Among them, B is the membership matrix, which has one row and four columns; F is the fuzzy comprehensive evaluation matrix, which has three rows and four columns; A is the weight matrix, which has one row and three columns. Its specific composition is as follows: The step S505 generates the membership of the enterprise risk in each risk level from the membership matrix, and the membership matrix is ​​as follows: Among them, b1 is the degree of membership when the enterprise risk level is low risk, b2 is the degree of membership when the enterprise risk level is medium risk, b3 is the degree of membership when the enterprise risk level is medium-high risk, and b4 is the degree of membership when the enterprise risk level is high risk.

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