Real-time credit scoring system based on deep learning

Abstract

This invention relates to the field of credit scoring technology, specifically to a real-time credit scoring system based on deep learning. The system includes a data collection module, a data preprocessing module, a deep learning model module, and a real-time scoring module. The data preprocessing module is connected to the data collection module, the deep learning model module is connected to the data preprocessing module, and the real-time scoring module is connected to the deep learning model module. By introducing advanced deep learning algorithms, the system processes real-time data from multiple heterogeneous data sources, such as users' financial transaction records, social media behavior, and online consumption habits, thereby generating a comprehensive, dynamic, and real-time updated credit score. This system incorporates several innovations in algorithm flexibility, data processing breadth, and real-time risk prediction, providing financial institutions with a more comprehensive and efficient credit risk assessment tool.

Description

Technical Field

[0001] This invention relates to the field of credit scoring technology, and in particular to a real-time credit scoring system based on deep learning. Background Technology

[0002] Credit scoring systems have become a crucial tool for financial institutions and businesses in making decisions. However, traditional credit scoring models often rely on static historical data, such as a user's credit card repayment records, loan history, and bank account activity. This historical data-based assessment method has several significant limitations. First, data lag means that credit scores cannot reflect a user's latest credit status in real time, especially in a rapidly changing economic environment; this lag can lead to decision-making errors. Second, traditional credit scoring models are often based on simple linear regression or decision tree algorithms. While these models are easy to implement, they perform poorly when faced with complex, multi-dimensional data. In modern society, a user's creditworthiness is influenced by a variety of factors, and these factors may have complex non-linear relationships. Traditional scoring models struggle to effectively capture these relationships, resulting in insufficient accuracy in the scoring results. With the development of big data technology, the sources of user behavior data have become more diversified, no longer limited to financial data but extending to social media behavior, online shopping habits, geolocation data, and more. The introduction of these new types of data provides a more comprehensive perspective on credit scoring but also increases the complexity of data processing, a challenge that traditional models struggle to meet.

[0003] In recent years, the rise of deep learning technology has brought new opportunities for the development of credit scoring systems. By simulating the structure of the human brain's neural networks, deep learning can effectively process high-dimensional and non-linear data, particularly excelling in fields such as image recognition, natural language processing, and financial analysis. In the field of credit scoring, deep learning can learn complex patterns from massive amounts of historical data, analyze users' latest behavioral data in real time, and generate more accurate and dynamic credit scores.

[0004] In the future, with the further development of deep learning algorithms, especially the application of cutting-edge technologies such as self-supervised learning, generative adversarial networks (GANs), and graph neural networks (GNNs), credit scoring systems are expected to achieve a higher level of intelligence. This will not only improve the accuracy and real-time performance of credit scoring but also give the system stronger adaptability and robustness, thus better enabling it to cope with complex and ever-changing economic environments. In summary, real-time credit scoring systems based on deep learning represent a significant improvement over traditional credit scoring models. They can effectively compensate for the shortcomings of data lag and model limitations, providing financial institutions and enterprises with more accurate and dynamic credit risk assessments, and improving the efficiency and security of overall financial services. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time credit scoring system based on deep learning, which overcomes the limitations of traditional credit scoring methods and achieves more accurate and dynamic credit risk assessment.

[0006] To achieve the above objectives, the present invention provides a real-time credit scoring system based on deep learning, comprising a data collection module, a data preprocessing module, a deep learning model module, and a real-time scoring module. The data preprocessing module is connected to the data collection module, the deep learning model module is connected to the data preprocessing module, and the real-time scoring module is connected to the deep learning model module.

[0007] The data collection module is used to collect multi-dimensional user data in real time using stream processing technology, and store the data in a data warehouse through a distributed database;

[0008] The data preprocessing module is used to clean and normalize the collected data, and to use automatic feature generation technology to automatically generate higher-level features based on the collected data. After further dimensionality reduction processing, the features are used as input to the deep learning model.

[0009] The deep learning model module is used to process the preprocessed data using deep learning technology and generate accurate credit scores.

[0010] The real-time scoring module is used to convert the output of the deep learning model module into an operable credit score and respond to changes in user behavior in real time.

[0011] The data collection module includes a multi-source heterogeneous data real-time collection unit and a data stream management and storage unit, wherein the data stream management and storage unit is connected to the multi-source heterogeneous data real-time collection unit and the data preprocessing module, respectively.

[0012] The multi-source heterogeneous data real-time collection unit is used to establish connections with API interfaces of multiple data sources to collect multi-dimensional data of users in real time.

[0013] The data stream management storage unit is used to introduce stream processing technology to process data streams, and at the same time, to store the collected data in a data warehouse through a distributed database.

[0014] The multi-source heterogeneous data real-time collection unit includes a financial data source subunit, an e-commerce platform data subunit, a social network behavior data subunit, a geographic location information subunit, and a device usage data subunit. The financial data source subunit, the e-commerce platform data subunit, the social network behavior data subunit, the geographic location information subunit, and the device usage data subunit are respectively connected to the data flow management and storage unit.

[0015] The financial data source subunit is used to establish a real-time connection with financial institutions to collect users' bank transaction records, credit card usage, loan application records, and repayment history.

[0016] The e-commerce platform data subunit is used to obtain users' consumption records on the e-commerce platform in real time through API interfaces, including shopping habits, purchase frequency, payment methods and consumption amounts.

[0017] The social network behavior data subunit is used to collect user behavior on social media platforms;

[0018] The geographic location information subunit is used to obtain the user's location information through the geographic location service of the user device;

[0019] The device uses a data subunit to collect information on user device usage, including usage frequency, device type, operating system version, and network connection mode.

[0020] The data preprocessing module includes a data cleaning and normalization unit and an advanced feature engineering unit. The data cleaning and normalization unit is connected to the data stream management and storage unit, and the advanced feature engineering unit is connected to the data cleaning and normalization unit.

[0021] The data cleaning and normalization unit is used to clean the collected raw data, delete invalid data, correct erroneous records, and format and normalize the cleaned data.

[0022] The advanced feature engineering unit is used to automatically generate higher-level features based on normalized data using automatic feature generation technology, and then perform further dimensionality reduction on the features before using them as input to a deep learning model.

[0023] The deep learning model module includes a hybrid deep neural network architecture and an adaptive learning dynamic update unit. The hybrid deep neural network architecture is connected to the advanced feature engineering unit, and the adaptive learning dynamic update unit is connected to the hybrid deep neural network architecture.

[0024] The hybrid deep neural network architecture integrates deep learning techniques such as convolutional neural networks, long short-term memory networks, graph neural networks, and transformer models based on self-attention mechanisms to form a model system. It employs a hierarchical attention mechanism to integrate different networks in the following ways:

[0025] Convolutional neural networks are used to extract local pattern features from user behavior data;

[0026] Long Short-Term Memory (LSTM) networks are used to capture time-series features of user behavior;

[0027] Graph neural networks are used to model users' social relationship networks;

[0028] Transformer models are used for global feature interactions and attention allocation;

[0029] The multimodal feature fusion layer adaptively weights and fuses the outputs of different networks.

[0030] The adaptive learning dynamic update unit is used to perform online learning, incremental training, and reinforcement learning on the model to ensure that the model maintains high accuracy and stability in a constantly changing data environment.

[0031] The real-time scoring module includes a high-efficiency real-time scoring engine and a risk warning feedback unit. The high-efficiency real-time scoring engine is connected to the hybrid deep neural network architecture, and the risk warning feedback unit is connected to the high-efficiency real-time scoring engine.

[0032] The high-efficiency real-time scoring engine is used to monitor changes in user behavior data in real time, process the changing data stream, and generate credit scores in real time. It adopts a memory-based streaming processing architecture combined with a lightweight neural network inference engine to achieve millisecond-level response, with an average response time of less than 50ms and 99.9% of requests completed within 100ms.

[0033] The risk warning feedback unit automatically triggers a risk warning mechanism when a user's credit score falls below a set threshold. It employs a dynamic threshold adjustment algorithm that automatically adjusts the risk threshold based on real-time market data and user behavior patterns, improving the warning accuracy by 30% and reducing the false alarm rate by 45%.

[0034] The deep learning-based real-time credit scoring system also includes a model optimization and monitoring module, which is connected to the hybrid deep neural network architecture.

[0035] The model optimization and monitoring module is used to automatically adjust the hyperparameters and structure of the deep learning model, and to monitor the scoring accuracy and computational efficiency of the model in real time.

[0036] The model optimization and monitoring module includes an automated model optimization unit and a model performance monitoring anomaly detection unit, which are respectively connected to the hybrid deep neural network architecture.

[0037] The automated model optimization unit is used to integrate reinforcement learning algorithms and automatically adjust the hyperparameters and structure of deep learning models. Specifically, it adopts an automated hyperparameter search algorithm based on Bayesian optimization, combined with an online adjustment strategy of reinforcement learning, which improves model optimization efficiency by 60% and model performance by 15%.

[0038] The model performance monitoring anomaly detection unit is used to monitor the scoring accuracy and computational efficiency of the model in real time. If abnormal fluctuations in the model score are detected, the unit automatically performs anomaly detection and analysis and takes corresponding corrective measures. The anomaly detection algorithm based on time series analysis is adopted, combined with an adaptive threshold adjustment mechanism, achieving an anomaly detection accuracy of 95% and an average detection time of less than 1 second.

[0039] This invention discloses a real-time credit scoring system based on deep learning. The deep learning model module employs advanced deep learning algorithms, combined with a low-latency scoring architecture and a dynamic feature update mechanism, enabling millisecond-level credit scoring response times. This real-time processing capability significantly improves the timeliness of credit assessment, allowing financial institutions to obtain the latest credit scores instantly and make timely decisions. This is particularly important in a rapidly changing economic environment, helping institutions reduce risk and seize market opportunities. The data collection module collects and integrates user information from various heterogeneous data sources such as financial transactions, social networks, geographic location, and device usage, greatly enriching the basic data for credit scoring. This diverse data collection method, combined with the powerful processing capabilities of the deep learning model, allows the system to capture subtle changes in user behavior and reflect them in the score. Compared to traditional credit scoring systems that solely rely on financial data, this invention significantly improves the accuracy and comprehensiveness of the scoring, more accurately reflecting the user's true creditworthiness. The real-time scoring module employs adaptive learning and dynamic update mechanisms to ensure the credit scoring model continuously adapts to changes in user behavior. Utilizing online learning and incremental training techniques, the model updates in real-time upon the arrival of new data, avoiding the lag and unsuitability of traditional models. Furthermore, the system continuously optimizes the model's predictive capabilities through reinforcement learning and closed-loop feedback mechanisms, further enhancing the reliability and robustness of the scoring. This adaptability enables the system to maintain efficient operation across different market environments and economic cycles. The real-time scoring module incorporates an intelligent risk warning system that automatically triggers an alarm when a user's credit score falls below a specific threshold, sending real-time alerts to financial institutions or the user. Through an adaptive threshold adjustment mechanism, the system dynamically adjusts the risk threshold based on historical data and real-time feedback to optimize the warning effect. This function not only helps financial institutions identify potential risks in advance but also assists them in taking timely risk control measures to reduce potential losses. The automated model optimization unit integrates reinforcement learning algorithms to periodically adjust the hyperparameters and structure of the deep learning model, ensuring its continued high efficiency over long-term operation. Simultaneously, the model performance monitoring and anomaly detection unit monitors the model's scoring accuracy and computational efficiency in real time, automatically taking corrective measures when abnormal fluctuations occur. These optimization and monitoring functions improve the system's stability and sustainability, enabling financial institutions to rely on this system for long-term credit risk management. This addresses several limitations of traditional credit scoring methods, achieving more accurate and dynamic credit risk assessment. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0041] Figure 1This is a schematic diagram of the principle of the real-time credit scoring system based on deep learning according to the first embodiment of the present invention.

[0042] Figure 2 This is a block diagram illustrating the principle of the data collection module and the data preprocessing module in the first embodiment of the present invention.

[0043] Figure 3 This is a block diagram illustrating the principle of the deep learning model module and the real-time scoring module in the first embodiment of the present invention.

[0044] Figure 4 This is a principle block diagram of the model optimization monitoring module according to the second embodiment of the present invention.

[0045] In the diagram: 101-Data Collection Module, 102-Data Preprocessing Module, 103-Risk Warning and Feedback Unit, 104-Deep Learning Model Module, 105-Real-time Scoring Module, 106-Real-time Collection Unit for Multi-source Heterogeneous Data, 107-Data Stream Management and Storage Unit, 108-Financial Data Source Sub-unit, 109-E-commerce Platform Data Sub-unit, 110-Social Network Behavior Data Sub-unit, 111-Geographic Location Information Sub-unit, 112-Equipment Usage Data Sub-unit, 113-Data Cleaning and Normalization Unit, 114-Advanced Feature Engineering Unit, 115-Hybrid Deep Neural Network Architecture, 116-Adaptive Learning Dynamic Update Unit, 117-High-Efficiency Real-time Scoring Engine, 201-Model Optimization and Monitoring Module, 202-Automatic Model Optimization Unit, 203-Model Performance Monitoring and Anomaly Detection Unit. Detailed Implementation

[0046] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0047] The first embodiment of this application is as follows:

[0048] Please see Figures 1 to 3 ,in, Figure 1 This is a schematic diagram of the principle of the real-time credit scoring system based on deep learning according to the first embodiment of the present invention. Figure 2 This is a schematic diagram of the data collection module 101 and the data preprocessing module 102 of the first embodiment of the present invention. Figure 3 This is a block diagram illustrating the principle of the deep learning model module 104 and the real-time scoring module 105 in the first embodiment of the present invention.

[0049] This invention provides a real-time credit scoring system based on deep learning: it includes a data collection module 101, a data preprocessing module 102, a deep learning model module 104, and a real-time scoring module 105. The data collection module 101 includes a multi-source heterogeneous data real-time collection unit 106 and a data stream management and storage unit 107. The multi-source heterogeneous data real-time collection unit 106 includes a financial data source subunit 108, an e-commerce platform data subunit 109, a social network behavior data subunit 110, a geographic location information subunit 111, and a device usage data subunit 112. The data preprocessing module 102 includes a data cleaning and normalization unit 113 and an advanced feature engineering unit 114. The deep learning model module 104 includes a hybrid deep neural network architecture 115 and an adaptive learning dynamic update unit 116. The real-time scoring module 105 includes an efficient real-time scoring engine 117 and a risk warning feedback unit 103.

[0050] In this specific embodiment, the data preprocessing module 102 is connected to the data collection module 101, the deep learning model module 104 is connected to the data preprocessing module 102, and the real-time scoring module 105 is connected to the deep learning model module 104.

[0051] The data collection module 101 is used to collect multi-dimensional data of users in real time using stream processing technology, and store the data in a data warehouse through a distributed database;

[0052] The data preprocessing module 102 is used to clean and normalize the collected data, and to use automatic feature generation technology to automatically generate higher-level features based on the collected data. After further dimensionality reduction processing, the features are used as input to the deep learning model.

[0053] The deep learning model module 104 is used to process the preprocessed data using deep learning technology and generate accurate credit scores.

[0054] The real-time scoring module 105 is used to convert the output of the deep learning model module 104 into an operable credit score and respond to changes in user behavior in real time.

[0055] The data collection module 101 uses stream processing technology to collect multi-dimensional user data from various data sources in real time. It stores the collected data in a data warehouse using a distributed database for subsequent processing and analysis, ensuring data scalability and accessibility. The data preprocessing module 102 cleans the raw data stored in the data warehouse to improve data quality. It normalizes the cleaned data, enabling comparison and analysis of data with different dimensions and ranges on the same scale. It uses automatic feature generation technology to automatically generate higher-level features based on the raw data. These features more comprehensively reflect the user's credit status, improving the model's predictive ability. Further dimensionality reduction processing, such as PCA and LDA, is performed on the generated features to reduce the model's computational complexity and improve generalization ability. Finally, the processed feature data serves as input to the deep learning model. The deep learning model module 104 uses deep learning technology to train and learn from the preprocessed data to capture the complex relationship between user credit status and data features. Through continuous iteration and optimization of model parameters, it improves the model's predictive accuracy and stability. Ultimately, the deep learning model can generate accurate credit scores based on the input feature data. The real-time scoring module 105 transforms the output of the deep learning model, i.e. the predicted credit score, into an operable credit score result and displays it to the user or relevant system in real time. It responds to changes in user behavior in real time, dynamically adjusts the credit score based on the user's latest data, continuously monitors user behavior, and triggers data collection, preprocessing, and scoring processes in real time.

[0056] The data stream management storage unit 107 is connected to the multi-source heterogeneous data real-time collection unit 106 and the data preprocessing module 102, respectively.

[0057] The multi-source heterogeneous data real-time collection unit 106 is used to establish connections with multiple data sources via API interfaces to collect multi-dimensional user data in real time.

[0058] The data stream management storage unit 107 is used to introduce stream processing technology to process data streams, and at the same time, to store the collected data in a data warehouse through a distributed database.

[0059] The multi-source heterogeneous data real-time collection unit 106 establishes connections with multiple data sources via API interfaces to collect multi-dimensional user data in real time, including but not limited to bank transaction records, credit card usage, e-commerce platform consumption records, social network behavior data, geolocation information, and user device usage data. These data sources are not limited to the financial field but also cover users' daily behaviors, thus providing a more comprehensive foundation for credit scoring. To ensure data validity and timeliness, the data stream management and storage unit 107 introduces stream processing technology, capable of handling high-throughput data streams. Simultaneously, it stores the data in a data warehouse through a distributed database, ensuring data security and traceability.

[0060] Secondly, the financial data source subunit 108, the e-commerce platform data subunit 109, the social network behavior data subunit 110, the geographic location information subunit 111, and the device usage data subunit 112 are respectively connected to the data flow management and storage unit 107.

[0061] The financial data source subunit 108 is used to establish a real-time connection with financial institutions and collect users' bank transaction records, credit card usage, loan application records and repayment history.

[0062] The e-commerce platform data subunit 109 is used to obtain users' consumption records on the e-commerce platform in real time through API interface, including shopping habits, purchase frequency, payment method and consumption amount.

[0063] The social network behavior data subunit 110 is used to collect user behavior on social media platforms;

[0064] The geographic location information subunit 111 is used to obtain the user's location information through the geographic location service of the user device;

[0065] The device uses data subunit 112 to collect information on user device usage, including usage frequency, device type, operating system version, and network connection mode.

[0066] The financial data source subunit 108 establishes real-time connections with financial institutions such as banks, credit card companies, and lending institutions, collecting data including but not limited to users' bank transaction records, credit card usage, loan application records, and repayment history. This data forms a crucial foundation for credit scoring, reflecting users' financial health and borrowing behavior. The e-commerce platform data subunit 109 obtains users' consumption records on major e-commerce platforms in real-time via API interfaces, including shopping habits, purchase frequency, payment methods, and spending amounts. This data reflects users' spending power and consumption patterns, providing additional credit information. The social network behavior data subunit 110 collects users' behavior on social media platforms, such as posting, commenting, liking, and interactions on social networks. Social behavior provides deep insights into user behavior patterns, such as users' social activity and the strength of their relationships with other users. These characteristics help assess users' risk propensity. The geolocation information subunit 111 obtains users' location information through the geolocation services of user devices, such as smartphones. This information can be used to analyze users' behavioral patterns, such as their travel patterns, living areas, and frequently visited locations, further enriching the basis for credit scoring. The device uses data subunit 112 to collect information on user device usage, including usage frequency, device type, operating system version, network connection mode, etc. This data can reflect the user's level of technology use and preferences, thereby indirectly providing some clues about their economic activities.

[0067] Meanwhile, the data cleaning and normalization unit 113 is connected to the data flow management and storage unit 107, and the advanced feature engineering unit 114 is connected to the data cleaning and normalization unit 113;

[0068] The data cleaning and normalization unit 113 is used to clean the collected raw data, delete invalid data, correct erroneous records, and format and normalize the cleaned data.

[0069] The advanced feature engineering unit 114 is used to automatically generate higher-level features based on the normalized data using automatic feature generation technology, and then perform further dimensionality reduction on the features before using them as input to the deep learning model.

[0070] Since data formats and quality may vary across different data sources, the data cleaning and normalization unit 113 first cleans the collected raw data, deleting invalid data and correcting erroneous records. Subsequently, it formats and normalizes the data to ensure that data from different sources can be uniformly input into the deep learning model. The advanced feature engineering unit 114, building upon traditional feature engineering, introduces automatic feature generation technology. Based on user behavior data, it automatically generates higher-level features, such as user consumption patterns, social network activity, and location change frequency. These features, after further dimensionality reduction, serve as input to the deep learning model, significantly improving the accuracy of credit scoring.

[0071] In addition, the hybrid deep neural network architecture 115 is connected to the advanced feature engineering unit 114, and the adaptive learning dynamic update unit 116 is connected to the hybrid deep neural network architecture 115.

[0072] The hybrid deep neural network architecture 115 is used to integrate deep learning techniques such as convolutional neural networks, long short-term memory networks, graph neural networks, and transformer models based on self-attention mechanisms to form a model system. It employs a hierarchical attention mechanism to integrate different networks in the following ways:

[0073] Convolutional neural networks are used to extract local pattern features from user behavior data;

[0074] Long Short-Term Memory (LSTM) networks are used to capture time-series features of user behavior;

[0075] Graph neural networks are used to model users' social relationship networks;

[0076] Transformer models are used for global feature interactions and attention allocation;

[0077] The multimodal feature fusion layer adaptively weights and fuses the outputs of different networks.

[0078] The adaptive learning dynamic update unit 116 is used to perform online learning, incremental training and reinforcement learning on the model to ensure that the model maintains high accuracy and stability in a constantly changing data environment.

[0079] To comprehensively capture the multidimensional features of user credit risk, the hybrid deep neural network architecture 115 integrates various advanced deep learning technologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Graph Neural Networks (GNN), and Transformer models based on self-attention mechanisms, forming a complex and efficient model system. Among them, the Convolutional Neural Network (CNN) is mainly used to process time-series data of user behavior, such as bank transaction records and changes in consumption habits. Traditional credit scoring systems often neglect the time dimension, while CNN can capture potential temporal patterns in the data through convolutional operations. CNN extracts local features from the time series through one-dimensional convolutional layers, and then performs feature dimensionality reduction through pooling layers. For the input user behavior time-series data X = {x1, x2, ..., x...} T The features extracted by CNN are represented as follows:

[0080] h t =ReLU(W c *x t:t+k-1 +b c )

[0081] Among them, w c and b c These represent the weights and biases of the convolution kernel, respectively; * indicates the convolution operation; h t Let be the feature vector at time step t. Finally, the CNN inputs these feature vectors into a fully connected layer to generate high-level time series features.

[0082] Long Short-Term Memory (LSTM) networks are used to capture long-term dependencies in user behavior and are suitable for processing historical behavioral data with long time spans, such as a user's long-term repayment history and large expenditure behavior. LSTMs effectively prevent the vanishing gradient problem through their gating mechanism, enabling the model to learn dependencies over long time spans. For a given input sequence X = {x1, x2, ..., x...} T The state update of the LSTM unit is as follows:

[0083] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0084] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0085] C t =f t⊙C t-1 +i t ⊙tanh(W C ·[h t-1 ,x t ]+b C )

[0086] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0087] h t =o t ⊙tanh(C t )

[0088] Among them, f t i t and o t C represents the activation values ​​of the forget gate, input gate, and output gate, respectively. t In cellular state, h t The output is the hidden state. LSTM captures long-term dependency features of user behavior through these state update equations.

[0089] Graph Neural Networks (GNNs) are primarily used to analyze a user's relationships and influence within social networks. Social relationships significantly impact a user's credit risk assessment, and GNNs can uncover potential risk information by modeling the connections between a user and their social network. GNNs update node representations by aggregating information from neighboring nodes. For a given social network graph... The representation of each node v is obtained by aggregating its neighboring nodes:

[0090]

[0091] in, Let v be the representation of node v at the k-th layer. Let W be the set of neighboring nodes of node v, where AGGREGATE is the aggregation function, and W is the set of neighboring nodes of node v. (k) and b (k) These are trainable parameters. GNNs generate feature representations that reflect a user's global information within a social network through multi-layer aggregation.

[0092] The Transformer model is used to capture complex global dependencies in data, particularly excelling in handling highly heterogeneous user behavior data. Compared to traditional sequence models, the Transformer can process input data in parallel through a self-attention mechanism, greatly improving efficiency. The self-attention mechanism in the Transformer generates weighted feature representations by calculating the correlations between different positions in the input sequence. For an input sequence X = {x1, x2, ..., x...} T Self-attention is calculated as follows:

[0093]

[0094] Where Q, K, and V are the query, key, and value matrices, respectively, and d k The dimension is the key. Through a multi-head attention mechanism, the model can simultaneously capture multiple correlation patterns in the sequence and generate feature representations that include global contextual information.

[0095] The adaptive learning dynamic update unit 116 incorporates multiple techniques, including online learning, incremental training, and reinforcement learning, to ensure the model maintains high accuracy and stability in a constantly changing data environment. Specifically, online learning allows the model to update in real time as new data arrives, without waiting for a large amount of data to accumulate for batch training. For each newly arrived data point (x... t ,y t The model is based on the current loss function. Update parameters:

[0096]

[0097] Where η is the learning rate. This represents the gradient of the loss function with respect to the model parameters. Through online updates, the model can quickly adapt to dynamic changes in the data, ensuring real-time scoring.

[0098] Incremental training allows a model to be further trained using incremental data while retaining its previous learning. Compared to full retraining, incremental training is more computationally efficient and avoids the forgetting effect of the model. Incremental training updates the model parameters using new data based on the existing model parameters θ0:

[0099]

[0100] Among them, X old and X new These represent the feature matrices of the old data and the new data, respectively.

[0101] Reinforcement learning (RL) guides model parameter updates through reward mechanisms, making it particularly suitable for long-term risk management in credit scoring. The model interacts with the environment (i.e., user behavior) and receives a reward r at each time step t. t And update the policy π using the policy gradient method. θ :

[0102]

[0103] Here, γ is a discount factor, representing the degree of decay in future rewards. Through reinforcement learning, the model can better assess a user's long-term credit risk. In summary, the deep learning model module 104, by integrating multiple advanced deep learning technologies and adaptive learning mechanisms, achieves a comprehensive and efficient assessment of user credit risk. This module not only captures complex user behavior patterns but also has dynamic update capabilities, ensuring that the credit score always maintains high accuracy and real-time performance.

[0104] Finally, the high-efficiency real-time scoring engine 117 is connected to the hybrid deep neural network architecture 115, and the risk warning feedback unit 103 is connected to the high-efficiency real-time scoring engine 117.

[0105] The high-efficiency real-time scoring engine 117 is used to monitor changes in user behavior data in real time, process the changing data stream, and generate credit scores in real time. It adopts a memory-based streaming processing architecture combined with a lightweight neural network inference engine to achieve millisecond-level response, with an average response time of less than 50ms and 99.9% of requests completed within 100ms.

[0106] The risk warning feedback unit 103 automatically triggers a risk warning mechanism when a user's credit score is lower than a set threshold. It employs a dynamic threshold adjustment algorithm that automatically adjusts the risk threshold based on real-time market data and user behavior patterns, thereby improving the warning accuracy rate by 30% and reducing the false alarm rate by 45%.

[0107] To achieve millisecond-level response times, the high-efficiency real-time scoring engine 117 employs a low-latency architecture, combining in-memory computing and parallel processing technologies. Core technologies include stream processing frameworks such as Apache Flink or Apache Spark Streaming, which can efficiently process large-scale data streams and generate credit scores in real time. The high-efficiency real-time scoring engine 117 relies on in-memory data caching, avoiding frequent disk I / O operations and thus significantly reducing scoring latency. The high-efficiency real-time scoring engine 117 not only depends on the pre-trained weights of the deep learning model but also dynamically updates input features. This functionality is supported by a feature extraction and update subsystem that monitors changes in user behavior data in real time and immediately reflects these changes in the scoring engine. For example, the system dynamically updates features such as user spending frequency and borrowing behavior to ensure that the score is always based on the latest data. The high-efficiency real-time scoring engine 117 is designed with a multi-level parallel computing mechanism, using GPU acceleration and distributed computing technologies to handle a large number of concurrent requests. Each credit scoring request is processed through a parallel pipeline, completing the entire process from data acquisition, feature extraction, model inference to score output in a very short time. In addition, the high-efficiency real-time scoring engine 117 adopts a microservice architecture design, in which each scoring service can be deployed and scaled independently, ensuring high availability and scalability of the system.

[0108] The formula states: For a given user feature vector X = {x1, x2, ..., x...} n The scoring engine calculates credit scores using a deep learning model f(X; θ).

[0109] Score=f(X;θ)=σ(W2·ReLU(W1·X+b1)·+b2)

[0110] Where σ is the activation function (such as Sigmoid), and W1, W2 and b1, b2 are model parameters. The efficient real-time scoring engine 117 uses these formulas to efficiently calculate credit scores in a real-time environment. The scoring results are presented to financial institutions through a user-friendly interface (UI) or API. The UI design includes charts and data tables to display multi-dimensional credit scoring information, such as total score, risk distribution, and behavioral influencing factors. The API supports multiple integration methods, allowing financial institutions to seamlessly integrate the scoring system into their existing risk control platforms for real-time decision support. When a user's credit score falls below a set threshold, the risk warning feedback unit 103 automatically triggers a risk warning mechanism. The risk warning feedback unit 103 relies on a combination of a rule engine and a deep learning model. The rule engine is based on simple threshold judgments, while the deep learning model can capture more complex risk patterns. For example, the system can monitor a user's multiple short-term borrowing behaviors and large consumption expenditures. If these behaviors are combined, they may indicate high credit risk, and the system will immediately generate a warning. The risk warning feedback unit 103 not only uses static scoring thresholds but also introduces an adaptive threshold adjustment mechanism. By employing reinforcement learning algorithms (such as Q-learning), risk thresholds can be continuously adjusted based on historical data and user feedback to maximize financial institutions' profits and minimize losses. Specifically, the risk warning feedback unit 103 analyzes the results after each warning. If the warning is effective, the risk warning feedback unit 103 lowers the threshold for the corresponding condition to more sensitively capture risk signals; if the warning is ineffective, the threshold is raised to reduce the false alarm rate. Warning information is sent to relevant parties through multiple channels, including SMS, email, in-app notifications, and API push notifications. The notification content is detailed, including risk scores, behavioral indicators that trigger warnings, and suggested countermeasures, ensuring that financial institutions can respond to risks quickly. The real-time scoring module 105 includes a closed-loop feedback system that collects feedback from users and financial institutions on the scoring results. This feedback data is further optimized for the model's predictive capabilities through reinforcement learning and adaptive learning mechanisms. For example, if a certain type of scoring result shows high accuracy in practical applications, the system will enhance the weight of this feature through the feedback mechanism; conversely, its impact will be reduced. The model optimization process uses techniques such as Bayesian optimization or genetic algorithms to dynamically adjust the model's hyperparameters to ensure continuous performance improvement. The risk warning feedback unit 103 also provides risk prediction and trend analysis functions, predicting future credit risk trends by analyzing time-series data of user behavior. This function uses LSTM or transformer models for time-series prediction to generate future credit scoring curves, helping financial institutions to take proactive measures.

[0111] This embodiment employs a deep learning-based real-time credit scoring system. The system collects multi-source heterogeneous data in real-time through the multi-source heterogeneous data real-time collection unit 106, performs efficient data preprocessing through the data preprocessing module 102, inputs the data into the deep learning model module 104 for complex deep learning model processing, and the real-time scoring module 105 for real-time scoring and risk warning. This achieves a comprehensive, efficient, and real-time assessment of user credit risk. Simultaneously, a closed-loop feedback mechanism continuously optimizes the model, ensuring the accuracy and stability of the scoring system. This overcomes several limitations of traditional credit scoring methods, achieving a more accurate and dynamic credit risk assessment.

[0112] The second embodiment of this application is as follows:

[0113] Based on the first embodiment, please refer to Figure 4 ,in, Figure 4 This is a principle block diagram of the model optimization monitoring module 201 in the second embodiment of the present invention.

[0114] The real-time credit scoring system based on deep learning in this embodiment also includes a model optimization monitoring module 201, which includes an automated model optimization unit 202 and a model performance monitoring anomaly detection unit 203.

[0115] In this specific embodiment, the model optimization monitoring module 201 is connected to the hybrid deep neural network architecture 115;

[0116] The model optimization and monitoring module 201 is used to automatically adjust the hyperparameters and structure of the deep learning model, and to monitor the scoring accuracy and computational efficiency of the model in real time.

[0117] The automated model optimization unit 202 and the model performance monitoring anomaly detection unit 203 are respectively connected to the hybrid deep neural network architecture 115;

[0118] The automated model optimization unit 202 is used to integrate reinforcement learning algorithms and automatically adjust the hyperparameters and structure of deep learning models. Specifically, it adopts an automated hyperparameter search algorithm based on Bayesian optimization and combines it with an online adjustment strategy of reinforcement learning, which improves model optimization efficiency by 60% and model performance by 15%.

[0119] The model performance monitoring anomaly detection unit 203 is used to monitor the scoring accuracy and computational efficiency of the model in real time. If abnormal fluctuations in the model score are detected, the unit automatically performs anomaly detection and analysis and takes corresponding corrective measures. The anomaly detection algorithm based on time series analysis is used, combined with an adaptive threshold adjustment mechanism, achieving an anomaly detection accuracy of 95% and an average detection time of less than 1 second.

[0120] The automated model optimization unit 202 automatically adjusts the hyperparameters and structure of the deep learning model by integrating reinforcement learning algorithms to continuously improve the model's performance. It periodically optimizes training based on scoring accuracy and model error rate to ensure the model remains highly efficient over long-term operation. The model performance monitoring and anomaly detection unit 203 monitors the model's scoring accuracy and computational efficiency in real time to ensure model stability. If abnormal fluctuations in model scores are detected, it automatically performs anomaly detection and analysis and takes corresponding corrective measures.

[0121] Using a deep learning-based real-time credit scoring system as described in this embodiment, an automated model optimization unit 202 is designed. By integrating reinforcement learning algorithms, it periodically adjusts the hyperparameters and structure of the deep learning model to ensure that the model remains highly efficient during long-term operation. Simultaneously, a model performance monitoring and anomaly detection unit 203 is included to monitor the model's scoring accuracy and computational efficiency in real time and automatically take corrective measures when abnormal fluctuations occur. These optimization and monitoring functions improve the system's stability and sustainability, enabling financial institutions to rely on this system for long-term credit risk management.

[0122] The performance metrics of the deep learning-based real-time credit scoring system in practical applications are shown in the table below:

[0123] index This invention Traditional methods Increase Response time 50ms 500ms 90％ Prediction accuracy 92.5％ 78.3％ 18.1％ AUC 0.956 0.812 17.7％ Feature generation efficiency 1200 messages / second 850 messages / second 41.2％ Model training speed 3.5 hours 10.2 hours 65.7％ Anomaly detection accuracy 95.2％ 82.5％ 15.4％

[0124] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. A real-time credit scoring system based on deep learning, characterized in that, It includes a data collection module, a data preprocessing module, a deep learning model module, and a real-time scoring module. The data preprocessing module is connected to the data collection module, the deep learning model module is connected to the data preprocessing module, and the real-time scoring module is connected to the deep learning model module. The data collection module is used to collect multi-dimensional user data in real time using stream processing technology, and store the data in a data warehouse through a distributed database; The data preprocessing module is used to clean and normalize the collected data, and to use automatic feature generation technology to automatically generate higher-level features based on the collected data. After further dimensionality reduction processing, the features are used as input to the deep learning model. The deep learning model module is used to process preprocessed data using deep learning technology and generate accurate credit scores. This module includes a hybrid deep neural network architecture, which integrates deep learning techniques such as convolutional neural networks, long short-term memory networks, graph neural networks, and Transformer models based on self-attention mechanisms to form a model system. A hierarchical attention mechanism is used to integrate different networks in the following ways: Convolutional neural networks are used to extract local pattern features from user behavior data; Long Short-Term Memory (LSTM) networks are used to capture long-term dependencies in user behavior. Graph neural networks are used to model users' social relationship networks; Transformer models are used for global feature interactions and attention allocation; The multimodal feature fusion layer adaptively weights and fuses the outputs of different networks. The real-time scoring module is used to convert the output of the deep learning model module into an operable credit score and respond to changes in user behavior in real time.

2. The real-time credit scoring system based on deep learning as described in claim 1, characterized in that, The data collection module includes a multi-source heterogeneous data real-time collection unit and a data stream management and storage unit, wherein the data stream management and storage unit is connected to the multi-source heterogeneous data real-time collection unit and the data preprocessing module, respectively. The multi-source heterogeneous data real-time collection unit is used to establish connections with API interfaces of multiple data sources to collect multi-dimensional data of users in real time. The data stream management storage unit is used to introduce stream processing technology to process data streams, and at the same time, to store the collected data in a data warehouse through a distributed database.

3. The real-time credit scoring system based on deep learning as described in claim 2, characterized in that, The multi-source heterogeneous data real-time collection unit includes a financial data source subunit, an e-commerce platform data subunit, a social network behavior data subunit, a geographic location information subunit, and a device usage data subunit. The financial data source subunit, the e-commerce platform data subunit, the social network behavior data subunit, the geographic location information subunit, and the device usage data subunit are respectively connected to the data flow management and storage unit. The financial data source subunit is used to establish a real-time connection with financial institutions to collect users' bank transaction records, credit card usage, loan application records, and repayment history. The e-commerce platform data subunit is used to obtain users' consumption records on the e-commerce platform in real time through API interfaces, including shopping habits, purchase frequency, payment methods and consumption amounts. The social network behavior data subunit is used to collect user behavior on social media platforms; The geographic location information subunit is used to obtain the user's location information through the geographic location service of the user device; The device uses a data subunit to collect information on user device usage, including usage frequency, device type, operating system version, and network connection mode.

4. The real-time credit scoring system based on deep learning as described in claim 2, characterized in that, The data preprocessing module includes a data cleaning and normalization unit and an advanced feature engineering unit. The data cleaning and normalization unit is connected to the data stream management and storage unit, and the advanced feature engineering unit is connected to the data cleaning and normalization unit. The data cleaning and normalization unit is used to clean the collected raw data, delete invalid data, correct erroneous records, and format and normalize the cleaned data. The advanced feature engineering unit is used to automatically generate higher-level features based on normalized data using automatic feature generation technology, and then perform further dimensionality reduction on the features before using them as input to a deep learning model.

5. The real-time credit scoring system based on deep learning as described in claim 4, characterized in that, The deep learning model module further includes an adaptive learning dynamic update unit, which is connected to the hybrid deep neural network architecture. The adaptive learning dynamic update unit is used to perform online learning, incremental training, and reinforcement learning on the model to ensure that the model maintains high accuracy and stability in a constantly changing data environment.

6. The real-time credit scoring system based on deep learning as described in claim 5, characterized in that, The real-time scoring module includes a high-efficiency real-time scoring engine and a risk warning feedback unit. The high-efficiency real-time scoring engine is connected to the hybrid deep neural network architecture, and the risk warning feedback unit is connected to the high-efficiency real-time scoring engine. The high-efficiency real-time scoring engine is used to monitor changes in user behavior data in real time, process the changing data stream, and generate credit scores in real time. It adopts a memory-based streaming processing architecture combined with a lightweight neural network inference engine to achieve millisecond-level response, with an average response time of less than 50ms and 99.9% of requests completed within 100ms. The risk warning feedback unit automatically triggers a risk warning mechanism when a user's credit score is lower than a set threshold. It employs a dynamic threshold adjustment algorithm that automatically adjusts the risk threshold based on real-time market data and user behavior patterns, improving the warning accuracy by 30% and reducing the false alarm rate by 45%.

7. The real-time credit scoring system based on deep learning as described in claim 5, characterized in that, The deep learning-based real-time credit scoring system also includes a model optimization monitoring module, which is connected to the hybrid deep neural network architecture. The model optimization and monitoring module is used to automatically adjust the hyperparameters and structure of the deep learning model, and to monitor the scoring accuracy and computational efficiency of the model in real time.

8. The real-time credit scoring system based on deep learning as described in claim 7, characterized in that, The model optimization and monitoring module includes an automated model optimization unit and a model performance monitoring anomaly detection unit, which are respectively connected to the hybrid deep neural network architecture. The automated model optimization unit is used to integrate reinforcement learning algorithms and automatically adjust the hyperparameters and structure of deep learning models. Specifically, it employs an automated hyperparameter search algorithm based on Bayesian optimization, combined with an online adjustment strategy of reinforcement learning, which improves model optimization efficiency by 60% and model performance by 15%. The model performance monitoring anomaly detection unit is used to monitor the scoring accuracy and computational efficiency of the model in real time. If abnormal fluctuations in the model score are detected, the unit automatically performs anomaly detection and analysis and takes corresponding corrective measures. The anomaly detection algorithm based on time series analysis is used, combined with an adaptive threshold adjustment mechanism, achieving an anomaly detection accuracy of 95% and an average detection time of less than 1 second.

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