Intelligent railway freight loan risk early warning system based on LSTM neural network

Abstract

The invention discloses an intelligent railway freight loan risk early warning system based on an LSTM neural network. The intelligent railway freight loan risk early warning system comprises a data acquisition module which acquires data such as the transportation volume, the financial condition and the loan repayment record of a client in real time; the data preprocessing module is used for carrying out cleaning, normalization and feature extraction on the collected original data; the LSTM model training and prediction module is used for training an LSTM model based on historical data and performing default risk prediction by using the trained model; and the early warning output module is used for outputting an early warning signal according to a prediction result of the model and prompting a potential default risk. According to the invention, a long short-term memory (LSTM) neural network is adopted to carry out time sequence modeling and analysis on the transportation volume and financial data of railway freight customers. Compared with a traditional credit risk assessment method, the LSTM can more effectively capture the long-term dependency relationship of the time sequence data, and higher accuracy is provided for predicting the future default risk of the customer.

Description

Technical Field

[0001] The present invention relates to the technical field of railway transportation financial services, and particularly to an intelligent railway freight loan risk early warning system based on the LSTM neural network. Background Art

[0002] Traditional credit assessment and risk early warning methods, such as models based on linear regression or logistic regression, usually can only process data at a single point in time or basic statistical features, and it is difficult to effectively capture the time series characteristics of the data.

[0003] It is difficult to handle complex non-linear features. The transportation volume and financial status of customers often have complex non-linear features, and the traditional models have limited effects when dealing with these complex features, which may lead to prediction deviations. Dependence on manual work and low automation. Traditional methods usually require manual setting of thresholds and indicators / rules, relying on human experience for risk assessment and adjustment. Moreover, traditional methods usually rely on fixed credit scoring criteria or rules to evaluate the risks of customers, while the LSTM-based system can automatically learn risk patterns from historical data, predict the default risks of customers with higher accuracy, and effectively reduce the probabilities of false alarms and missed alarms. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the prior art, the present invention provides an intelligent railway freight loan risk early warning system based on the LSTM neural network. This system collects the transportation volume and financial data of railway freight customers in real time, models and analyzes the time series through the LSTM model, predicts the possible default risks of customers in the future, and provides early warnings in advance according to the prediction results.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: An intelligent railway freight loan risk early warning system based on the LSTM neural network, including a data collection module: collecting data such as the transportation volume, financial status, and loan repayment records of customers in real time;

[0008] A data preprocessing module: cleaning, normalizing, and extracting features from the collected original data;

[0009] An LSTM model training and prediction module: training an LSTM model based on historical data and using the trained model for default risk prediction;

[0010] An early warning output module: outputting an early warning signal according to the prediction result of the model to prompt potential default risks.

[0011] Preferably, the data preprocessing module extracts important features using the principal component analysis (PCA) algorithm, such as historical transportation volume, loan balance, and repayment status.

[0012] Preferably, the LSTM model solves the problem of gradient disappearance in traditional RNN for long sequence processing by introducing "memory cells" and three gating mechanisms (forget gate, input gate, and output gate) that control the information flow.

[0013] Preferably, the early warning output module optimizes the parameters in the LSTM using the gradient descent algorithm by minimizing the loss function, thereby improving the prediction accuracy of the model.

[0014] (III) Beneficial effects

[0015] Compared with the prior art, the present invention provides an intelligent railway freight loan risk early warning system based on the LSTM neural network, which has the following beneficial effects:

[0016] 1. The intelligent railway freight loan risk early warning system based on the LSTM neural network uses the long short-term memory (LSTM) neural network to perform time series modeling and analysis on the transportation volume and financial data of railway freight customers. Compared with traditional credit risk assessment methods, LSTM can more effectively capture the long-term dependence relationships in time series data, providing higher accuracy for predicting the future default risk of customers.

[0017] 2. The intelligent railway freight loan risk early warning system based on the LSTM neural network integrates various source data such as the transportation volume and financial data of railway freight customers, and comprehensively analyzes the correlations between various types of data using the LSTM model to generate comprehensive and complete risk early warning indicators; the real-time default risk prediction and early warning mechanism can collect customer data in real time and perform LSTM model prediction. This system can identify potential default risks in advance and provide real-time early warning information to relevant parties, which helps to reduce financial risks and prevent potential losses.

[0018] 3. The intelligent railway freight loan risk early warning system based on the LSTM neural network can effectively capture the time dependence and trend changes of customer data by using the LSTM neural network to perform time series modeling on the transportation volume and financial data of railway freight customers, providing accurate default risk prediction. LSTM has obvious advantages compared with traditional models in processing long sequence data, thereby improving the prediction accuracy.

[0019] 4. The intelligent railway freight loan risk early warning system based on the LSTM neural network can generate early risk warnings in advance based on the prediction results of the LSTM model, helping the relevant parties of the railway freight loan business to take preventive measures before the customers have default risks. Such an early warning mechanism can effectively reduce the loan default rate and reduce financial losses.

[0020] 5. The intelligent railway freight loan risk early warning system based on the LSTM neural network can dynamically adjust the risk assessment over time based on the self-learning characteristics of the LSTM neural network, adapt to changes in customer behavior and market environment, provide more flexible risk management means for the railway freight loan business, and enhance the ability to respond to emergencies. Detailed implementation manners

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] An intelligent railway freight loan risk early warning system based on the LSTM neural network includes a data collection module: collecting data such as the transportation volume, financial status, and loan repayment records of customers in real time;

[0023] A data preprocessing module: cleaning, normalizing, and feature extracting the collected original data;

[0024] An LSTM model training and prediction module: training the LSTM model based on historical data and using the trained model to predict default risks;

[0025] An early warning output module: outputting an early warning signal according to the prediction results of the model to prompt potential default risks.

[0026] The data preprocessing module uses the principal component analysis (PCA) algorithm to extract important features, such as historical transportation volume, loan balance, and repayment status.

[0027] Calculate the covariance matrix of the data set X. Assume that the data set X is an n×m matrix (n samples, m features). First, perform a centering process on each column (each feature) of X, that is, subtract the mean of the column to obtain X c . The covariance matrix

[0028] Calculate the eigenvalues λ i and eigenvectors v i, the eigenvalues are obtained by solving the characteristic equation |∑ - λI| = 0, and the corresponding eigenvectors are found.

[0029] Sort the eigenvalues from largest to smallest, and select the eigenvectors corresponding to the top k largest eigenvalues to form the projection matrix P (k is the number of principal components to be retained).

[0030] Project the original data X through the projection matrix P to obtain a new dataset Y = XP. Each column of the new dataset Y is a principal component, and the first few principal components are selected as important features.

[0031] The LSTM model solves the vanishing gradient problem of traditional RNNs in processing long sequences by introducing "memory cells" and three gating mechanisms (forget gate, input gate, and output gate) that control the information flow.

[0032] Mathematical definition and processing of time series data:

[0033] Let the transportation volume and financial data of an enterprise be represented as a time series X = {x 1 , x 2 , …, x t}, where x t represents the observed value at time t (such as transportation volume, income, repayment status, etc.), and these data usually exhibit significant time series dependence.

[0034] To analyze using the LSTM model, first, these time series data need to be standardized and corresponding training samples need to be constructed. Let y t be the default risk label corresponding to time t (such as default occurred or normal repayment). The goal of the model is to predict the default risk at the future time t + 1 by observing {x t-n , x t-n+1 , …, x t}. Core mathematical formulas of the LSTM neural network:

[0035] The LSTM neural network solves the vanishing gradient problem of traditional RNNs in processing long sequences by introducing "memory cells" C t and three gating mechanisms (forget gate, input gate, and output gate) that control the information flow. The main update formulas of LSTM are as follows:

[0036] Forget gate f t : Determines which information needs to be forgotten.

[0037] f t = σ(W f · [h t-1 , x t + b f );

[0038] Input gate i t : Decide which new information needs to be added to the memory cell.

[0039] i t = σ(W i ·[h t-1 , x t + b i );

[0040] Candidate memory Determine how to update the cell state through the input gate.

[0041]

[0042] Memory cell state update:

[0043]

[0044] Output gate O t : Determine the output result.

[0045] O t = σ(W O ·[h t-1 , x t + b o );

[0046] Final hidden state:

[0047] h t = O t · tanh(C t ).

[0048] Through these formulas, the LSTM can extract long-term and short-term temporal dependencies from the input traffic volume and financial data, and finally generate prediction results.

[0049] Prediction and optimization objective:

[0050] To apply the LSTM to the default risk prediction of railway freight loans, we define a loss function to measure the default risk predicted by the model and the difference from the true label y t+1 . The commonly used loss function is the mean squared error (MSE):

[0051]

[0052] By minimizing the loss function L, we can optimize the parameters W f , W i , W O , W C, thereby improving the prediction accuracy of the model.

[0053] The early warning output module optimizes the parameters in the LSTM by minimizing the loss function and using the gradient descent algorithm, thereby improving the prediction accuracy of the model.

[0054] Specific implementation cases

[0055] Example 1: Risk early warning for small and medium-sized logistics enterprises

[0056] Data collection: Collect data such as the transportation volume, income, and loan repayment records of a small and medium-sized logistics enterprise in the past year. The specific data includes:

[0057] Transportation volume: 800 tons to 1200 tons

[0058] Average monthly income: 200,000 yuan to 400,000 yuan

[0059] Loan balance: Fluctuates between 500,000 yuan and 1 million yuan per month

[0060] Data preprocessing: Normalize the data to ensure that the feature values input to the model are within a unified range. The extracted features include: the change rate of transportation volume, the trend of income increase or decrease, the historical repayment status, etc.

[0061] Model training and prediction: Train the LSTM model based on the transportation volume and financial data of the past 6 months. The model predicts that the probability of default risk in the next 3 months is 30%.

[0062] Early warning output: According to the model prediction results, the system sends a yellow early warning signal to the financial department, recommending strengthening the loan management of this enterprise.

[0063] Technical effect: Through this embodiment, the system realizes the dynamic monitoring of enterprise financial and transportation data and timely predicts the default risk. The financial department takes preventive measures in advance, such as adjusting the repayment plan, effectively reducing the loan default rate and improving the risk control ability.

[0064] Example 2: Risk early warning for large manufacturing enterprises

[0065] Data collection: Collect the transportation volume, financial statements, and loan repayment records of a large manufacturing enterprise in the past two years. The specific data includes:

[0066] Annual average transportation volume: More than 100,000 tons

[0067] Annual income: Exceeding 500 million yuan

[0068] Loan balance: Between 50 million yuan and 100 million yuan per month

[0069] Loan balance: Between 50 million yuan and 100 million yuan per month

[0070] Data preprocessing: After normalization, features are extracted, such as the seasonal variation of transportation volume, the correlation between income and transportation volume, historical repayment records, etc.

[0071] Model training and prediction: Use the data of the past 12 months to train the LSTM model, and predict that the default risk probability for the next 6 months is 5%.

[0072] Early warning output: Since the predicted risk is low, the system does not issue an early warning signal, but only provides the data to the background monitoring for subsequent business analysis.

[0073] Technical effect: This embodiment demonstrates the monitoring ability of the system for low-risk enterprises, avoids unnecessary early warnings, and improves the efficiency of credit services. In addition, through continuous data monitoring and analysis, it provides refined management support for financial institutions and optimizes the overall process of loan services.

[0074] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent railway freight loan risk early warning system based on LSTM neural network, characterized in that: Includes data collection module: real-time collection of customer transportation volume, financial status, loan repayment records and other data; Data preprocessing module: cleans, normalizes and extracts features from the collected raw data; LSTM model training and prediction module: train the LSTM model based on historical data and use the trained model to predict default risk; Early warning output module: Based on the prediction results of the model, it outputs early warning signals to indicate potential default risks.

2. According to claim 1, an intelligent railway freight loan risk early warning system based on LSTM neural network is characterized in that: The data preprocessing module uses the principal component analysis (PCA) algorithm to extract important features, such as historical transportation volume, loan balance, and repayment status.

3. According to claim 1, an intelligent railway freight loan risk early warning system based on LSTM neural network is characterized in that: The LSTM model solves the gradient vanishing problem of traditional RNN in long sequence processing by introducing "memory cells" and three gating mechanisms (forget gate, input gate and output gate) to control information flow.

4. According to claim 1, an intelligent railway freight loan risk early warning system based on LSTM neural network is characterized in that: The warning output module optimizes the parameters in LSTM by minimizing the loss function and using the gradient descent algorithm, thereby improving the prediction accuracy of the model.

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