A supply chain financial transaction security early warning method and system

Abstract

The present invention relates to the field of data processing technology, and the present invention relates to a supply chain financial transaction security early warning method and system, comprising: obtaining financial data of the supply chain and its corresponding risk score, dividing the financial data into continuous data and discrete data, and calculating its importance coefficient. The importance coefficient is positively correlated with the Spearman rank correlation coefficient of the risk score, and is negatively correlated with the Pearson correlation coefficient in continuous data, the standardized mutual information in discrete data, and the point biserial correlation coefficient between continuous data and discrete data. An improved XGBoost model is used to predict the probability of risk type of financial data, and the risk type corresponding to the maximum value of the probability is the final risk type of the financial data. The present invention solves the problem of inaccurate risk assessment of supply chain financial transactions by improving the XGBoost model.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology. More specifically, the present invention relates to a supply chain financial transaction security early warning method and system. Background Art

[0002] With the continuous development of supply chain finance, transactions between businesses are becoming increasingly frequent, capital flows are becoming more complex, and the risks associated with financial transactions are also increasing. In supply chain finance, in particular, the transaction chain between different businesses is long and involves numerous links, from raw material supply to production and manufacturing to the point of sale. A disruption in the capital chain at any link can impact the stability of the entire supply chain. Therefore, real-time monitoring of risk factors in the transaction process and effective early warning have become key to ensuring the security of supply chain finance. This not only improves the overall efficiency of the supply chain but also reduces the economic losses caused by risks to financial institutions and businesses, possessing significant practical significance and promising application prospects.

[0003] Currently, supply chain finance transaction security alerts primarily rely on analysis of corporate financial data and credit rating assessments. However, traditional analytical methods, mostly based on static financial statements and historical data, struggle to reflect dynamic changes in a company in real time. This can lead to overlooking potential risks, especially when the external market environment shifts. Furthermore, different companies have varying types of financial data, with some data being continuous and others discrete. This creates a technical challenge in effectively processing and integrating this data.

[0004] Faced with such a complex data structure, traditional risk assessment models struggle to accurately capture the risk signals hidden behind the data. XGBoost, among others, is a highly efficient gradient boosting decision tree algorithm model that iteratively trains multiple decision trees to gradually reduce the prediction error of the previous model. Each iteration adjusts the model based on the previous prediction error, allowing the new decision tree to focus more on difficult-to-predict samples. However, XGBoost is sensitive to hyperparameters, and different hyperparameter configurations can lead to significant differences in model performance. Complex tuning processes are often required to ensure the model achieves optimal performance on a specific dataset. This is especially true when applied to different datasets, often requiring extensive experiments to find the optimal parameter configuration. Consequently, uncertainty in parameter configuration often leads to inaccurate risk assessments for supply chain finance transactions. Summary of the Invention

[0005] In order to solve the problem raised in the above background technology that the uncertainty of parameter configuration often leads to inaccurate risk assessment of supply chain financial transactions, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a supply chain financial transaction security early warning method, comprising:

[0007] Obtain financial data of the supply chain and its corresponding risk score, and divide the financial data into continuous data and discrete data; calculate the importance coefficient of the financial data, wherein the importance coefficient is positively correlated with the Spearman rank correlation coefficient of the risk score, and is negatively correlated with the Pearson correlation coefficient in continuous data, the normalized mutual information in discrete data, and the point-biserial correlation coefficient between continuous data and discrete data; use the improved XGBoost model to monitor the risk of the financial data to obtain the final probability of each risk type to which the financial data belongs; wherein the improved XGBoost model includes: calculating the weight of each classification decision tree in the XGBoost model, wherein the weight is positively correlated with the importance coefficient and negatively correlated with the number of financial data assigned to each classification decision tree; performing weighted averaging on the weight of each classification decision tree and the probability of each risk type output by each classification decision tree to obtain the final probability of each risk type to which the financial data belongs, and the risk type corresponding to the maximum value of the final probability is the final risk type of the financial data.

[0008] The above technical solution calculates the importance coefficient of financial data by utilizing multiple correlation analysis methods, thereby optimizing and improving the weight distribution of classification decision trees in the XGBoost model. At the same time, it reduces the bias of a single decision tree through weighted averaging, thereby improving the accuracy and robustness of the prediction of various risk types of financial data.

[0009] Furthermore, the importance coefficient C of the i-th financial data is:

[0010]

[0011] Where r i represents the Spearman rank correlation coefficient between the i-th financial data and its corresponding risk score, α represents the weight factor, ln represents the logarithmic function with base e, r represents the Pearson correlation coefficient in continuous data, NMI represents the normalized mutual information in discrete data, and R represents the point-biserial correlation coefficient between continuous and discrete data.

[0012] This technical solution accurately assesses the importance of each piece of financial data by introducing a calculation formula based on the Spearman rank correlation coefficient, the Pearson correlation coefficient, the normalized mutual information, and the point-biserial correlation coefficient. By using a logarithmic smoothing function and incorporating a weighting factor α, it achieves a comprehensive balance between continuous and discrete data, thereby more accurately capturing the complex relationship between financial data and risk scores.

[0013] Furthermore, the weight D of the k-th classification decision tree k for:

[0014]

[0015] Where C i represents the importance coefficient of the i-th financial data, and M represents the number of financial data assigned to the k-th classification decision tree.

[0016] This technical solution associates the weights of classification decision trees with the importance coefficient of financial data, ensuring that the weight of each decision tree reflects the importance of the data it processes. This not only strengthens the focus on high-importance data, but also reduces the negative impact of low-quality decision trees on the overall performance of the model.

[0017] Furthermore, the continuous data includes: debt-to-asset ratio, current ratio, quick ratio, order cycle, transaction volume standard deviation and transaction amount; the discrete data includes: number of transactions, number of overdue payments, customer credit score and number of historical defaults.

[0018] Furthermore, the specific process of risk monitoring of the financial data is as follows:

[0019] An XGBoost model is constructed and trained to obtain a trained XGBoost model; the financial data is input into the trained XGBoost model to obtain a final probability of the risk type to which the financial data belongs.

[0020] Furthermore, the risk types include: high risk type, medium risk type and low risk type.

[0021] Furthermore, it also includes using spline interpolation method to preprocess financial data.

[0022] In a second aspect, the present invention provides a supply chain financial transaction security early warning system, comprising a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a supply chain financial transaction security early warning method described above is implemented.

[0023] The beneficial effects of the present invention are:

[0024] This paper divides financial data into continuous and discrete data and combines the Spearman rank correlation coefficient, Pearson correlation coefficient, normalized mutual information, and point-biserial correlation coefficient to determine the data importance coefficient, thereby improving the feature selection effect of financial data. At the same time, the improved XGBoost model performs a weighted average of the probabilities of each risk type based on the importance coefficient and the weight of the classification decision tree, improving the model's classification accuracy and ability to identify different risk types, effectively enhancing the risk monitoring capabilities of the supply chain finance system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0026] Figure 1 is a flow chart schematically illustrating a supply chain financial transaction security early warning method according to an embodiment of the present invention;

[0027] Figure 2 is a block diagram schematically illustrating a structure of a supply chain financial transaction security early warning system according to an embodiment of the present invention; DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] An embodiment of a supply chain financial transaction security early warning method.

[0031] like Figure 1 As shown, a flow chart of a supply chain financial transaction security early warning method according to an embodiment of the present invention includes the following steps:

[0032] S1: Obtain financial data of the supply chain and its corresponding risk score, and divide the financial data into continuous data and discrete data.

[0033] In this embodiment, a set of financial data of one or more enterprises and their corresponding risk scores are obtained from a supply chain finance system through an application programming interface. The risk scores can be obtained through manual calibration, and of course, the financial data can also be input into a machine learning model and then output; the financial data is divided into continuous data and discrete data based on the continuity of the values; and the financial data is preprocessed using the spline interpolation method; among them, the continuous data includes: debt-to-asset ratio, current ratio, quick ratio, order cycle, transaction standard deviation and transaction amount; the discrete data includes: number of transactions, number of overdue payments, customer credit score and number of historical defaults.

[0034] S2: Calculate the importance coefficient of financial data.

[0035] In one embodiment, the importance coefficient is positively correlated with the Spearman rank correlation coefficient of the risk score, and is negatively correlated with the Pearson correlation coefficient in continuous data, the normalized mutual information in discrete data, and the point-biserial correlation coefficient between continuous and discrete data; the importance coefficient C of the i-th financial data is i for:

[0036]

[0037] Where r i represents the Spearman rank correlation coefficient between the i-th financial data and its corresponding risk score, α represents the weight factor, ln represents the logarithmic function with base e, r represents the Pearson correlation coefficient in continuous data, NMI represents the normalized mutual information in discrete data, and R represents the point-biserial correlation coefficient between continuous and discrete data.

[0038] For example: If the Spearman rank correlation coefficient between the second debt-to-asset ratio and its risk score is 0.6, the Pearson correlation coefficient in continuous data is 0.4, the standardized mutual information in discrete data is 0.3, the point biserial correlation coefficient between continuous data and discrete data is 0.25, and the weight factor α is 0.5, substituting the above values into the importance coefficient formula for calculation, the importance coefficient of the debt-to-asset ratio is 0.693.

[0039] S3: Use the importance coefficient to quantify the weight of each classification decision tree in the XGBoost model to obtain a trained XGBoost model.

[0040] In one embodiment, when building an XGBoost model, a set of models containing multiple classification decision trees is used, and each decision tree is assigned a weight based on different financial data. In order to improve the accuracy of the model, different weights are assigned to each tree, and the weights of each classification decision tree in the XGBoost model are calculated. The weights are positively correlated with the importance coefficient and negatively correlated with the number of financial data assigned to each classification decision tree. Among them, the weight D of the kth classification decision tree is k for:

[0041]

[0042] Where C i represents the importance coefficient of the i-th financial data, and M represents the number of financial data assigned to the k-th classification decision tree.

[0043] For example, if the first classification decision tree is assigned to five financial data, and their corresponding importance coefficients are 0.693, 0.55, 0.48, 0.6, and 0.4, the weight of the first classification decision tree is calculated as:

[0044]

[0045] Similarly, the weights of other decision trees can be calculated and used to train the XGBoost model. The training data is the financial data and its corresponding importance coefficients in the above steps. During the training process, the model adjusts its classification effect according to the weight of each classification decision tree to improve the overall classification accuracy, thereby obtaining a trained XGBoost model.

[0046] S4: Use the trained XGboost model to output the final probability of each risk type to which the financial data belongs, and perform risk monitoring on the financial data.

[0047] In this embodiment, the acquired financial data is input into a trained XGboost model, and the weights of each classification decision tree in the model and the probabilities of each risk type output by each classification decision tree are weighted averaged to obtain the final probability of each risk type to which the financial data belongs. The risk types include high risk type, medium risk type, and low risk type.

[0048] The specific process of risk monitoring is: building an XGBoost model, training the XGBoost model, and obtaining a trained XGBoost model;

[0049] Input the financial data into the trained XGBoost model to obtain the final probability of the risk type to which the financial data belongs.

[0050] For example, assuming the risk monitoring results of a piece of financial data: the probability of high-risk type is 40%, the probability of medium-risk type is 35%, and the probability of low-risk type is 25%. Based on the maximum value of the risk probability, the final risk type of the financial data is determined. Since the probability of high-risk type is the largest, the financial data is judged to be of high-risk type.

[0051] In this embodiment, all financial data can be automatically monitored for risk types, and early warning signals can be issued based on high-risk data. For example, upon detecting high-risk data, the system can automatically generate a report and notify relevant personnel to take appropriate measures, such as increasing scrutiny, adjusting credit limits, or taking other security measures, thereby providing continuous early warning of potential risks.

[0052] The solution of the present invention achieves precise risk monitoring of supply chain finance data by combining multiple statistical correlation coefficients with an improved XGBoost model. By distinguishing continuous and discrete data and calculating importance coefficients, the model's ability to distinguish data features is effectively enhanced. The improved XGBoost model can more accurately classify financial data based on the weights of each classification decision tree and the output risk probability, thereby improving the accuracy and reliability of risk monitoring. This approach not only enhances the model's adaptability to complex data but also enables more effective identification and early warning of potential risks.

[0053] An embodiment of a supply chain financial transaction security early warning system:

[0054] like Figure 2 As shown, a structural block diagram of a supply chain financial transaction security early warning system according to an embodiment of the present invention includes a processor and a memory.

[0055] The present invention also provides a supply chain financial transaction security early warning system. Figure 2 As shown, the system includes a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, a supply chain financial transaction security early warning method according to the above-mentioned present invention is implemented.

[0056] The supply chain financial transaction security early warning system also includes other components familiar to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0057] In the present invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible or connectable to a device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions that can be stored or otherwise retained by such a computer-readable medium.

[0058] In the description of this specification, "multiple" and "several" mean at least two, such as two, three or more, etc., unless otherwise clearly defined.

[0059] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

Claims

1. A supply chain financial transaction security early warning method, characterized in that: include: Obtaining financial data of the supply chain and its corresponding risk score, and dividing the financial data into continuous data and discrete data; Calculating a significance coefficient for the financial data, wherein the significance coefficient is positively correlated with the Spearman rank correlation coefficient of the risk score and negatively correlated with the Pearson correlation coefficient for continuous data, the normalized mutual information for discrete data, and the point-biserial correlation coefficient between continuous and discrete data; No. The importance coefficient of financial data for: )； Where, Indicates the The Spearman rank correlation coefficient between financial data and its corresponding risk score, represents the weight factor, represents the logarithmic function with base e, represents the Pearson correlation coefficient in continuous data, represents the normalized mutual information in discrete data, Represents the point-biserial correlation coefficient between continuous and discrete data; Using the improved XGBoost model to perform risk monitoring on the financial data, and obtain the final probability of each risk type to which the financial data belongs; The improved XGBoost model includes: calculating the weight of each classification decision tree in the XGBoost model, wherein the weight is positively correlated with the importance coefficient and inversely correlated with the number of financial data assigned to each classification decision tree; performing a weighted average of the weight of each classification decision tree and the probability of each risk type output by each classification decision tree to obtain the final probability of each risk type to which the financial data belongs, and the risk type corresponding to the maximum value of the final probability is the final risk type of the financial data; The specific process of risk monitoring of the financial data is as follows: Constructing an XGBoost model, and training the XGBoost model to obtain a trained XGBoost model; Input the financial data into the trained XGBoost model to obtain the final probability of the risk type to which the financial data belongs.

2. A supply chain financial transaction security early warning method according to claim 1, characterized in that: No. The weight of the classification decision tree for: ； Where, Indicates the The importance coefficient of financial data, Indicates the The number of financial data points assigned to the classification decision tree.

3. A supply chain financial transaction security early warning method according to claim 1, characterized in that: The continuous data includes: debt-to-asset ratio, current ratio, quick ratio, order cycle, transaction volume standard deviation and transaction amount; the discrete data includes: number of transactions, number of overdue payments, customer credit score and number of historical defaults.

4. A supply chain financial transaction security early warning method according to claim 1, characterized in that: Risk types include: high risk type, medium risk type and low risk type.

5. A supply chain financial transaction security early warning method according to claim 1, characterized in that: It also includes data preprocessing of financial data using spline interpolation method.

6. A supply chain financial transaction security early warning system, characterized by: The method comprises a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a supply chain financial transaction security early warning method as described in any one of claims 1 to 5 is implemented.

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