Method and system for risk control modelling and deployment

Abstract

In a described embodiment, a system for predictive modeling is provided. The system includes a data input module configured to acquire raw data and a data pre-processing module 5 configured to process the raw data to generate sample data. The system further includes a feature engineering module configured to generate derived features using one or more specified criterion and a model generation and optimization module configured to generate and refine a predictive model using a plurality of machine learning techniques. A model evaluation module is configured to assess model performance using predefined metrics. A 10 model tuning system is configured to iteratively adjust model parameters of the predictive model based on performance feedback and a model deployment module is configured to implement the predictive model in a production environment.

Description

[0001] Method and System for Risk Control Modelling and Deployment

[0002] TECHNICAL FIELD

[0003] The present disclosure relates generally to risk management systems, and in particular to methods and systems for risk control modelling and deployment that process data, generate and optimize models for diverse risk scenarios.

[0004] BACKGROUND

[0005] In various fields, organizations utilize data analysis and machine learning techniques for risk control. The development of big data technologies, improvements in computational power, and algorithmic enhancements have led to the adoption of intelligent risk control systems. These systems, particularly those employing machine learning models, have shown improved accuracy and feature recognition capabilities compared to traditional data analysis methods in risk control management and data mining across multiple sectors.

[0006] The implementation of machine learning models in risk control often depends on the quality of input data, feature selection, and model parameters. Users of these systems need to understand both the data and the models in depth. This requirement increases the complexity and cost associated with using machine learning for data analysis in risk control scenarios.

[0007] The creation of customized machine learning models for different scenarios and groups further complicates the model-building process. This customization introduces several technical challenges in the field of risk control modelling. One such challenge is the high technical barrier for modelling. The development of risk control models requires expertise in mathematical statistics, machine learning, computer programming, and practical modelling experience. This combination of skills is often found only in large organizations or specialized consulting firms, limiting the accessibility of sophisticated risk control modelling for smaller entities. Another issue is the low efficiency of the modelling process. The development and refinement of risk control models involve complex procedures, long development cycles, and significant computational resource investments. Manual modelling requires substantial time for various stages, including data pre-processing, model selection, variable selection, parameter tuning, and model evaluation. Model interpretability presents an additional technical challenge. Many applications require some level of interpretability in their risk control models. While certain models offer interpretability, they are more complex to develop. Many high-performing machine learning models operate as "black boxes," with internal mechanisms that are difficult to interpret. Furthermore, the accessibility of these systems for non-expert users is limited. Various departments within an organization may need to perform data analysis and modelling for different scenarios. However, personnel in many of these departments often lack the specialized modelling capabilities and experience necessary to effectively utilize advanced risk control systems.

[0008] Therefore, it is desirable to provide a system and method that enhances risk control modelling and deployment to address the technical disadvantages or limitations of the existing technologies or, at the very least, provide the public with a useful alternative.

[0009] SUMMARY

[0010] Embodiments of the present disclosure provide a method and system for risk control modelling and deployment that automate an end-to-end process of predictive model development for risk management scenarios. The system integrates technologies in data pre-processing, feature engineering, hyperparameter tuning, and model deployment into a unified platform for risk control applications. This integration allows users — including non-expert users and personnel from various departments — to efficiently develop and deploy sophisticated risk control models. By automating complex tasks requiring expert knowledge, the system reduces technical barriers, improves modelling efficiency, enhances model interpretability, and empowers non-expert users to develop customized risk control models.

[0011] In accordance with a first aspect of the present disclosure, a system for predictive modeling is provided. The system includes a data input module configured to acquire raw data; a data pre-processing module configured to process the raw data to generate sample data; a feature engineering module configured to generate derived features using one or more specified criterion; a model generation and optimization module configured to generate and refine a predictive model using a plurality of machine learning techniques; a model evaluation module configured to assess model performance using predefined metrics; a model tuning system configured to iteratively adjust model parameters of the predictive model based on performance feedback; and a model deployment module configured to implement the predictive model in a production environment.

[0012] In an embodiment, the system for predictive modeling includes a user interface module configured to facilitate interaction between a user and the system.

[0013] In an embodiment, the data pre-processing module is configured to process at least one of numerical data, categorical data, or time-series data. In an embodiment, the one or more specified criterion used by the feature engineering module includes at least one of a data type constraint criterion, a feature coverage criterion, or a semantic criterion.

[0014] In an embodiment, the model deployment module is configured to deploy the predictive model using containerized distributed deployment.

[0015] In an embodiment, the model tuning system is configured to maintain a record of a plurality of model versions of the predictive model, including their respective performance metrics.

[0016] According to a second aspect of the present disclosure, a method for predictive modeling is provided. The method includes: obtaining raw data comprising multiple feature values corresponding to various features and their respective labels; processing the raw data to generate sample data suitable for modeling, the processing including applying feature derivation to generate derived features based on specified derivation rules using one or more criteria; generating and optimizing a predictive model using the sample data; evaluating the predictive model using predetermined evaluation metrics; and deploying the predictive model in a production environment.

[0017] In an embodiment, the one or more criteria used in the feature derivation include at least one of a data type constraint criterion, a feature coverage criterion, or a semantic criterion.

[0018] In an embodiment, processing the raw data further includes at least one of: selecting data based on specified features, sampling the selected data according to specified sampling rules, encoding the selected data using a specified encoding method, identifying missing values in the selected data; processing outliers in the selected data, and filtering duplicates to generate a final sample data.

[0019] In an embodiment, the sampling rules include at least one of random sampling, stratified sampling, or oversampling.

[0020] In an embodiment, the encoding method is selected from one-hot encoding, weight of evidence encoding, or ordinal variable encoding.

[0021] In an embodiment, the predetermined evaluation metrics include at least one of Kolmogorov- Smirnov statistic, Gini coefficient, receiver operating characteristic curve, area under the curve, or population stability index.

[0022] According to a third aspect of the present disclosure, a method for generating a predictive model is provided. The method includes: presetting one or more types of predictive model algorithms and hyperparameter search settings; training the predictive model using sample data based on predetermined evaluation metrics; implementing hyperparameter search using a specified search method and search range; applying feature selection to identify relevant features for the predictive model; applying binning operations to the sample data; and evaluating the predictive model to determine optimal algorithm types and hyperparameter values based on the predetermined evaluation metrics.

[0023] In an embodiment, the hyperparameter search method is selected from tree-structured Parzen estimator, grid search, random search, simulated annealing, naive evolutionary method, batch optimization, or black-box optimization.

[0024] In an embodiment, the predictive model algorithms are selected from logistic regression, extreme gradient boosting, boosting machine, gradient boosting, naive Bayes, decision tree, or random forest.

[0025] In an embodiment, the feature selection method is selected from L1 regularization, random forest algorithm, or indicator judgment method.

[0026] In an embodiment, the binning operations are selected from decision tree optimal binning or chi-squared binning.

[0027] According to a fourth aspect of the present disclosure, a method for tuning a predictive model is provided. The method includes: configuring a hyperparameter search space fora predictive model; initiating a model tuning process using a machine learning framework; generating model hyperparameters within the hyperparameter search space; training the predictive model using the generated hyperparameters; evaluating the performance of the trained predictive model using predefined evaluation metrics; iteratively repeating the steps of generating hyperparameters, training, and evaluating until a predefined criterion is met.

[0028] In an embodiment, the machine learning framework is configured to perform parameter tuning using an optimization method selected from Bayesian optimization, genetic algorithms, or reinforcement learning.

[0029] In an embodiment, the method for tuning the predictive model includes maintaining a record of model versions and their respective performance metrics during the tuning process.

[0030] Embodiments of the present disclosure may be provided as a network of communicating devices (i.e. a “computerized network”). Embodiments of the invention may be also provided as a software application downloadable into a computer device to facilitate the method. The software application may be a computer program product, which may be stored on a non- transitory computer-readable medium on a tangible data-storage device (such as a storage device of a server, or one within a user device).

[0031] BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In the following, embodiments of the present invention will be described as non-limiting examples with reference to the accompanying drawings in which:

[0033] FIG. 1 is a block diagram illustrating a system architecture for predictive modelling and deployment, according to an embodiment of the present disclosure.

[0034] FIG. 2 is a flowchart illustrating a high-level predictive modelling and deployment process implementable by the system architecture of FIG. 1 , according to an embodiment of the present disclosure.

[0035] FIG. 3 is a flowchart illustrating a process for generating sample data from raw data for predictive modelling, according to an embodiment of the present disclosure.

[0036] FIG. 4 is a flowchart illustrating a process for generating and optimizing a predictive model, according to an embodiment of the present disclosure.

[0037] FIG. 5 is a flowchart illustrating an alternative process for generating and optimizing a predictive model including a model tuning framework, according to an embodiment of the present disclosure.

[0038] FIG. 6 is a block diagram illustrating a technical architecture of a computing server for implementing the processes of FIGs 2-5, according to an embodiment of the present disclosure.

[0039] DETAILED DESCRIPTION

[0040] In the following, embodiments of the present invention will be described as non-limiting examples with reference to the accompanying drawings in which:

[0041] As used herein, the term "risk control model" refers to a predictive algorithm or mathematical representation designed to assess, quantify, and manage potential risks in various business scenarios. These models may incorporate machine learning techniques, statistical methods, and domain-specific knowledge to analyse data patterns and make predictions about potential risks.

[0042] As used herein, the term "feature engineering" refers to the process of creating new variables or modifying existing ones from raw data to enhance the predictive power of machine learning models. This process may include techniques such as polynomial feature generation, principal component analysis, or domain-specific feature creation based on business rules.

[0043] As used herein, the term "hyperparameter" refers to a parameter whose value is set before a learning process begins, distinguishing it from other parameters that are determined through training. Examples of hyperparameters include learning rate, number of hidden layers in a neural network, or the number of trees in a random forest algorithm.

[0044] As used herein, the term "AutoML" or "Automated Machine Learning" refers to the process of automating the end-to-end process of applying machine learning to real-world problems. In the context of the disclosure, AutoML approaches may be used to efficiently explore the model and hyperparameter space, potentially utilizing methods like Bayesian optimization or genetic algorithms.

[0045] As used herein, the term "containerized deployment" refers to a method of software deployment in which an application is packaged, along with its dependencies and configuration, into a standardized unit for software development called a container. This approach, utilizing technologies like Docker, enables consistent operation across different computing environments.

[0046] As used herein, the term "feature interaction" refers to the combined effect of two or more features on the outcome variable that is different from the individual effects of these features. These interactions may be evaluated through cross-validation procedures, wherein various feature combinations and their relationships are iteratively tested to identify those that maximize predictive accuracy and contribute most substantially to the performance of the risk control model.

[0047] As used herein, the term "model interpretability" refers to the degree to which a human can understand the cause of a machine learning model's decision. In the context of this disclosure, model interpretability may be enhanced through techniques such as generating interpretable rule combinations extracted from model results.

[0048] As used herein, the term "risk control scenario" refers to a specific situation or context in which risk assessment and management are applicable or beneficial. This may encompass various types of risk, including but not limited to credit risk, market risk, operational risk, or other business-specific risk situations. The predictive modelling and deployment process described herein may be adapted to analyse, predict, and provide insights for these diverse risk scenarios, allowing for customized risk control strategies across different business domains. As used herein, the term "data pre-processing" refers to the set of techniques used to transform raw data into a clean and processable format. This may include operations such as data selection, sampling, binning, encoding, imputing missing values, processing outliers, and removing duplicates.

[0049] As used herein, the term "model tuning framework" refers to a systematic approach for optimizing the performance of predictive models. This framework includes steps such as configuring the hyperparameter search space, generating and evaluating model hyperparameters, and iteratively refining the model based on performance metrics.

[0050] As used herein, the term "binning" refers to a data pre-processing technique where continuous numerical data is grouped into discrete categories or bins. This technique can be useful for certain types of models, such as logistic regression, in processing non-linear relationships and reducing the impact of outliers.

[0051] As used herein, the term "model versioning" refers to the practice of tracking changes to machine learning models over time. This process may include maintaining a history of model iterations, their respective hyperparameters, and performance metrics, allowing for comparison between versions and the ability to rollback to previous versions if necessary.

[0052] As used herein, the term "model deployment" refers to the process of integrating a trained machine learning model into a production environment where it can take in an input and return an output. In the context of this disclosure, this process may involve containerized deployment for distributed computing.

[0053] As used herein, the term "cross-validation" refers to a resampling procedure used to evaluate machine learning models on a limited data sample. This technique may be used in the system for assessing how the results of a statistical analysis will generalize to an independent data set.

[0054] As used herein, the term "feature derivation" refers to the process of creating new features from existing ones based on specified rules or criteria. This process may employ a multicriteria approach, including data type constraint, feature coverage, and semantic criteria, to ensure the quality and relevance of derived features.

[0055] As used herein, "feature coverage" refers to the prevalence or distribution of feature values within a dataset. In the context of feature derivation, if the coverage of a derived feature falls below a pre-set threshold, the related derivation logic may be filtered out to maintain data quality. As used herein, "semantic criterion" in feature derivation refers to the assessment of the meaning and relevance of derived features based on their source features and the corresponding derivation logic. This criterion ensures that derived features are not only statistically significant but also meaningful in the context of risk control.

[0056] As used herein, the "data type constraint criterion" in feature derivation refers to a rule that ensures the derivation logic conforms to the possible derivation methods for the involved data types. This criterion analyses the data types of both the source features and the potential derived features to determine if the set derivation logic aligns with the permissible derivation methods for those data types. For example, it may define how numerical, categorical, or timeseries data can be combined or transformed to create new features.

[0057] As used herein, the "feature coverage criterion" in feature derivation refers to a quantitative measure used to evaluate the prevalence and distribution of feature values within a dataset. This criterion assesses whether a derived feature has sufficient representation across the dataset to be considered meaningful and reliable. Specifically, if the coverage of a derived feature — that is, the proportion of non-null or non-zero values — falls below a pre-set threshold (for example, 5% of the applicable data points), the related derivation logic may be filtered out. This helps maintain data quality by ensuring that derived features are well-represented and potentially informative across the dataset, rather than being relevant to only a small subset of cases.

[0058] As used herein, the term "hyperparameter tuning" refers to the process of optimizing the hyperparameters of a machine learning model to improve its performance. This process may involve systematic searches through a manually specified subset of the hyperparameter space of a learning algorithm, guided by predetermined evaluation criteria. Techniques for hyperparameter tuning may include grid search, random search, Bayesian optimization, or evolutionary algorithms, among others.

[0059] As used herein, the term "model evaluation metrics" refers to quantitative measures used to assess the performance and reliability of machine learning models. These metrics may vary depending on the type of problem (e.g., classification, regression) and the specific requirements of the risk control scenario. Common metrics may include, but are not limited to, accuracy, precision, recall, F1-score, area under the ROC curve (AUC), Kolmogorov-Smirnov (KS) statistic, and mean squared error. In the context of this disclosure, these metrics play a crucial role in model selection, hyperparameter tuning, and ongoing model performance monitoring. Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. For instance, the claimed subject matter may be implemented as a computer-readable medium embedded with a computer executable program, which encompasses a computer program accessible from any computer-readable storage device or storage media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips . . .), optical disks (e g., compact disk (CD), digital versatile disk (DVD) . . .), smart cards, and flash memory devices (e.g., card, stick, key drive . . .).

[0060] FIG. 1 is a block diagram illustrating a system architecture 100 for predictive modelling and deployment, according to an embodiment of the present disclosure. The system architecture 100 includes interconnected modules with automated workflows configured to implement a predictive modelling and deployment process, with a particular focus on risk control scenarios. For example, the system architecture 100 may include a User Interface Module 102, a Data Input Module 104, a Data Pre-processing Module 106, a Feature Engineering Module 108, a Model Generation and Optimization Module 110, a Model Evaluation Module 112, a Model Tuning System 114, and a Model Deployment Module 116. These modules may be configured to operate in automated coordination to manage an entire lifecycle of predictive model development, from data input to model deployment.

[0061] The User Interface Module 102 facilitates interaction between users and the system 100. The User Interface Module 102 may be configured to provide interfaces for data upload, parameter configuration, and results visualization. In implementations, the User Interface Module 102 may offer role-based access control, allowing different levels of interaction based on user expertise. For example, a data scientist might have full access to all model parameters, while a business analyst might interact with a simplified interface focused on high-level inputs and outputs. The User Interface Module 102 may be configured to enable users, including nonexpert users, to access and utilize modelling capabilities.

[0062] The Data Input Module 104 is configured to manage the ingestion of data into the system 100. The Data Input Module 104 may support various data formats and sources, including structured databases, semi-structured JSON or XML files, and unstructured text data. The Data Input Module 104 may implement robust error processing and validation mechanisms to ensure data integrity from the outset. The Data Input Module 104 acquires raw data from files uploaded by users or retrieves data from designated databases, ensuring flexibility in data sourcing. Upon acquiring the raw data, the Data Input Module 104 may automatically identify the data types of the features (e.g., numerical, categorical, time-series) and generates an analysis report summarizing key statistical metrics of the dataset. This analysis report provides users with an overview of the data's characteristics, facilitating informed decision-making during the modeling process. An example of an analysis report generated by the Data Input Module 104 is shown in Table 1 below.

[0063] Table 1

[0064] The Data Pre-processing Module 106 is configured to prepare the raw data for analysis. The Data Pre-processing Module 106 may perform a series of operations including data selection, data sampling, encoding, imputing missing values, mitigating outliers, and removing duplicates. For example, the Data Pre-processing Module 106 might process missing values through imputation techniques, normalize numerical features to a common scale, and encode categorical variables using methods like one-hot encoding or label encoding. The Data Preprocessing Module 106 operations help ensure the quality and reliability of the data used in subsequent modelling steps.

[0065] The Data Pre-processing Module 106 is configured to automatically process diverse data types often encountered in risk control scenarios, including numerical, categorical, and timeseries data. For numerical data, the module 106 may apply normalization or standardization techniques. Categorical data might be encoded using methods like one-hot encoding or label encoding. Time-series data could be processed to extract relevant features such as trends or seasonality. For mitigating outliers, the module 106 can employ methods such as isolation forest detection, Z-score detection, or custom filtering. Missing values can be processed through methods including deletion, interpolation (such as mean and median padding), or model prediction padding

[0066] The Feature Engineering Module 108 is configured to enhance the predictive power of the models by employing various techniques to create new features or transform existing ones. For example, these techniques may include methods such as polynomial feature generation, principal component analysis for dimensionality reduction, or domain-specific feature creation based on business rules. The Feature Engineering Module 108 may leverage automated feature selection algorithms to identify the most relevant features for the predictive task, minimizing the need for manual intervention.

[0067] The Feature Engineering Module 108 may employ an automated, multi-criteria approach to feature derivation. This approach includes automated application of data type constraints, feature coverage analysis, and semantic relevance assessment. The feature derivation process implements semantic analysis and relevance criteria for feature selection, ensuring that the derived features are both statistically significant and meaningful in the context of risk control. This automated approach ensures that derived features are not only statistically significant but also meaningful and relevant in the context of risk control, while significantly reducing the need for manual feature engineering.

[0068] Data Type Constraint Criterion: This criterion ensures that the derivation logic conforms to the permissible operations for the data types involved. The module 108 may automatically determines the data types of both the source features and potential derived features, verifying whether the specified derivation logic aligns with the possible derivation methods for those data types. For instance, numerical features may be combined using arithmetic operations, while categorical features might be transformed through encoding or cross-feature techniques.

[0069] Feature Coverage Criterion: This criterion evaluates the prevalence of feature values within the dataset. The module 108 may automatically assess whether a derived feature has sufficient representation across the dataset to be considered meaningful and reliable. If the coverage of a derived feature falls below a preset threshold (e.g., less than 5% of the dataset), the related derivation logic is filtered out to maintain data quality.

[0070] Semantic Criterion: This criterion involves assessing the semantic meaning of derived features based on their source features and the corresponding derivation logic. The module 108 may automatically analyse whether the derived features are not only statistically significant but also meaningful in the context of risk control. Features that lack semantic relevance or interpretability are excluded to maintain the model's overall quality and applicability. Example of Feature Derivation: Consider the features "Occupation" and "Age" from the dataset. The system can automatically derive a new feature, "Career Development Expectation," by combining these two features. This derived feature provides insights into a customer's potential for income growth and stability, which are pertinent factors in assessing credit risk. By ensuring that: the derivation logic conforms to data type constraints (combining categorical "Occupation" with numerical "Age" appropriately), the derived feature has sufficient coverage across the dataset, and the feature holds semantic relevance in the risk control scenario, the system enhances the model's predictive performance while maintaining interpretability.

[0071] The Feature Engineering Module 108 can further automate the processing of feature interactions by evaluating the impact of different feature combinations on model performance. This may be performed through automated cross-validation techniques, allowing the system to identify and select the most informative feature interactions for the risk control model. For example, the module 108 can automatically assess how combinations of features like 'income', 'credit score', and 'employment duration' might interact to provide more predictive power than these features individually. By systematically and automatically evaluating these feature interactions and combinations, the Feature Engineering Module 108 can automatically select the optimal feature set that maximizes the model's predictive performance while maintaining interpretability, all without requiring deep expertise from the user.

[0072] The Model Generation and Optimization Module 110 is configured to provide predictive modelling capabilities. For example, the Model Generation and Optimization Module 110 may implement various machine learning algorithms, including statistical methods, ensemble techniques, and deep learning models. The Model Generation and Optimization Module 110 may use techniques such as cross-validation and hyperparameter tuning to optimize model performance. For hyperparameter tuning, the module 110 may support various search methods, including tree-structured Parzen estimator, grid search method, random search method, simulated annealing method, naive evolutionary method, batch optimization method, and black-box optimization method. A retry threshold can be set for the system search process to avoid excessive consumption of system resources. Furthermore, the Model Generation and Optimization Module 110 may employ automated machine learning (AutoML) approaches to efficiently explore the model and hyperparameter space, potentially utilizing methods like Bayesian optimization or genetic algorithms. This automated approach reduces technical barriers to model development, enabling users to generate sophisticated risk control models without deep expertise in machine learning techniques. The Model Evaluation Module 112 may be configured to assess the performance and reliability of the generated models. The Model Evaluation Module 112 may calculate various performance metrics appropriate for the specific predictive task, such as accuracy, precision, recall, F1 -score for classification tasks, or mean squared error and R-squared for regression tasks. In the context of risk control, the Model Evaluation Module 112 might place particular emphasis on metrics like the area under the ROC curve (AUC) or the Kolmogorov-Smirnov (KS) statistic. The Model Evaluation Module 112 may enhance model interpretability by generating comprehensive reports that include interpretable rule combinations extracted from model results. This feature is particularly valuable for non-expert users who need to understand and explain model decisions. For instance, it might provide clear, businessrelevant explanations of how different factors contribute to risk assessments, making complex model outputs accessible to a wider range of stakeholders.

[0073] The Model Tuning System 114 is configured to fine-tune model parameters based on performance feedback, implementing iterative optimization techniques to continuously improve model performance over time. To achieve this, the Model Tuning System 114 may leverage automated machine learning (AutoML) tools, such as Neural Network Intelligence (NNI), to streamline the hyperparameter tuning and model selection processes. This automation significantly reduces the need for manual intervention, enhancing modelling efficiency and enabling rapid optimization of predictive models.

[0074] Automated Hyperparameter Tuning:

[0075] Configuring the Hyperparameter Search Space: The Model Tuning System 114 may allow users to define the range and distribution of hyperparameters to be explored. For example, this may include specifying parameters such as learning rates, regularization coefficients, the number of layers or nodes in neural networks, and other algorithm-specific settings. Users can customize the search space based on the specific requirements of the risk control model, ensuring that the exploration is both comprehensive and relevant.

[0076] Initiating the AutoML Framework: Upon configuring the hyperparameter search space, the system may initiate the AutoML tool (e.g., NNI). The AutoML framework can orchestrate the hyperparameter optimization process, utilizing advanced algorithms like Bayesian optimization, genetic algorithms, or reinforcement learning to efficiently navigate the hyperparameter space. This intelligent exploration identifies promising hyperparameter configurations that are likely to enhance model performance.

[0077] Generating and Evaluating Hyperparameters: The AutoML tool systematically generates hyperparameter configurations and trains models using these settings. Each generated model is evaluated against predefined performance metrics relevant to risk control, such as the Kolmogorov-Smirnov (KS) statistic, Gini coefficient, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUG), and Population Stability Index (PSI). This evaluation provides quantitative measures of each model's effectiveness in managing risk.

[0078] Iterative Optimization: Based on the evaluation results, the Model Tuning System 114 may iteratively refine the hyperparameters, focusing the search on configurations that demonstrate superior performance. For example, this process may continue until a stopping criterion is met, such as achieving a desired performance threshold, reaching a maximum number of iterations, or exhausting the allocated computational resources. The iterative nature of this optimization ensures that the system converges towards the most effective hyperparameter settings.

[0079] Domain-Specific Constraints and Business Rules: To ensure that the optimized models remain practically applicable in real-world risk control scenarios, the Model Tuning System 114 may incorporate domain-specific constraints and business rules. These constraints guide the optimization process, ensuring that the resulting models adhere to industry standards and organizational policies. For example, certain hyperparameter settings may be restricted to maintain model interpretability or compliance with regulatory requirements.

[0080] Model Versioning and Performance Tracking: The Model Tuning System 114 may facilitate model versioning and performance tracking over time. Each iteration of the predictive model generated during the tuning process is assigned a unique version identifier. For example, the system 114 records detailed metadata associated with each model version, including hyperparameter settings, subsets of training data used, feature sets employed, and performance evaluations based on predefined metrics. This metadata is stored in a version control repository or database, enabling easy retrieval and comparison of different model versions. Users can monitor how model performance evolves as new data becomes available or as underlying risk patterns change, ensuring that the most effective models are deployed for risk control management.

[0081] Enhancing Modelling Efficiency:

[0082] By automating the hyperparameter tuning and model selection processes, the Model Tuning System 114 significantly enhances modelling efficiency. Automated parameter tuning allows for the intelligent exploration of the hyperparameter space, reducing the computational resources and time typically required for manual optimization. This efficiency enables organizations to develop and deploy high-performing risk control models swiftly, responding effectively to evolving business needs and risk landscapes. The Model Deployment Module 116 is configured to automatically manage the process of putting the developed models into a production environment. The Model Deployment Module 116 may implement automated model versioning, ensuring that the system can track different iterations of models and roll back if necessary. In example embodiments, the Model Deployment Module 116 utilizes containerized deployment technologies, such as Docker. This approach enables the module to deploy the model using automated containerized distributed deployment, facilitating parallel execution of multiple experimental tasks.

[0083] The use of containerized deployment technologies, such as Docker, enables the Model Deployment Module 116 to implement the predictive model using automated containerized distributed deployment. This approach facilitates parallel execution of multiple experimental tasks, offering significant advantages in terms of high availability, reliability, and scalability. By automating the management of deployment environments, the system ensures consistent performance across various computational infrastructures and enables efficient scaling to handle varying loads in risk control applications.

[0084] The use of containerized deployment offers advantages of high availability, reliability, and scalability in risk control applications by automating managing the deployment lifecycle. This automated approach to deployment significantly enhances the system's ability to handle complex risk control scenarios across diverse computational environments. It allows for seamless scaling of resources based on demand, ensuring optimal performance even under varying workloads. Furthermore, the containerized deployment facilitates easier maintenance and updates of the risk control models, as each model can be isolated and managed independently, reducing potential conflicts and simplifying the overall system architecture.

[0085] In the production environment, the predictive model can be automatically integrated with an organization's existing infrastructure through well-defined interfaces, such as application programming interfaces (APIs) or messaging systems. This integration allows other applications or services to send input data to the model and receive predictions in real-time or batch processing modes, further enhancing the automation and efficiency of the risk control process.

[0086] While the system's core functionality remains consistent across different risk types, it can adapt to specific requirements of various risk scenarios such as credit risk, market risk, or operational risk. This adaptation is primarily achieved through the configuration of specific processing methods and parameters for each risk type. For example, when pre-processing data for certain risk types, special attention might be given to deduplication, while for others, processing missing data might be more critical. Similarly, the system can apply different sampling rules (such as random sampling, stratified sampling, or oversampling) based on the characteristics of the risk type being analysed.

[0087] FIG. 2 is a flowchart illustrating a high-level predictive modelling and deployment process 200 implementable by the system architecture of FIG. 1, according to an embodiment of the present disclosure.

[0088] As shown in FIG. 2, at step 202, the process 200 obtains raw data. This step may be executed by the Data Input Module 104 of the system architecture 100 illustrated in FIG. 1. The Data Input Module 104 may acquire raw data from various sources, such as files uploaded by users or designated databases. The raw data may include multiple feature values corresponding to various features and their respective risk control labels. This step facilities in providing data that can be used in subsequent modelling steps of process 200.

[0089] At step 204, the raw data is processed to generate sample data. This step may be performed by the Data Pre-processing Module 106 and the Feature Engineering Module 108 shown in FIG. 1. For example, the Data Pre-processing Module 106 receives raw data as input and may perform operations including data selection, data sampling, encoding, treating missing values, processing outliers, and removing duplicates. The output of the Data Pre-processing Module 106 is may be a structured dataset with consistent format and scale. This structured dataset may then serve as input to the Feature Engineering Module 108. The Feature Engineering Module 108 may then perform feature derivation, generating derived features based on specified feature derivation rules in order to generate a final sample dataset. This step prepares data for model training, enhancing the quality of the risk control models. The resulting sample data includes features that capture patterns and relationships in the data, potentially improving the predictive performance of the models.

[0090] At Step 206 a predictive model is generated and optimized. The generation and optimization of the model may be implemented by the Model Generation and Optimization Module 110, potentially in conjunction with the Model Tuning System 114 of FIG. 1. At step 206, one or more types of risk control model algorithms and / or model hyperparameter search settings may be pre-set. The sample data from step 204 may be used for training based on predetermined first model evaluation metrics to generate a risk control model for managing risks in the business. The optimization process may employ an automated approach that significantly reduces the technical barriers to model development, allowing even non-expert users to generate sophisticated risk control models. The automation may include techniques such as cross-validation and hyperparameter tuning to optimize model performance, and use methods like Bayesian optimization or genetic algorithms to efficiently explore the model and hyperparameter space. In step 208, the predictive model is tested. This step may be performed by the Model Evaluation Module 112 illustrated in FIG. 1. The testing process may include evaluating the model using test data based on predetermined evaluation metrics. These metrics may include statistical measures such as the Kolmogorov-Smirnov (KS) statistic, Gini coefficient, Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), and Population Stability Index (PSI). This step assesses the model's performance and reliability to ensure it meets required standards before deployment.

[0091] Finally, at step 210, the predictive model is deployed. This step may be processed by the Model Deployment Module 116 shown in FIG. 1. The deployment process may include containerized distributed deployment, enabling parallel execution of multiple tasks. This approach may offer advantages in availability, reliability, and scalability, allowing the system to process varying loads while maintaining consistent performance.

[0092] Throughout the process 200 illustrated in FIG. 2, user interaction may be provided to facilitate data input, parameter configuration, and results visualization. This interaction enables users of system 100 to access modelling capabilities, making risk control modelling accessible to a wider range of users.

[0093] FIG. 3 is a flowchart illustrating a process 204 for generating sample data from raw data for predictive modelling, according to an embodiment of the present disclosure. This process 204 may be implemented by the Data Pre-processing Module 106 and Feature Engineering Module 108 of the system architecture 100 shown in FIG. 1.

[0094] At step 302, the process 204 selects data. This step may include selecting a first data set from the raw data based on a set of specified features. For example, users may set thresholds to exclude data associated with invalid features or select specified features to include data associated with those features. The data selection process may process diverse data types often encountered in risk control scenarios, including numerical, categorical, and time-series data. Each data type may require specific pre-processing techniques. For instance, numerical data might be normalized or standardized, categorical data could be encoded using methods like one-hot encoding, and time-series data might be processed to extract relevant features such as trends or seasonality.

[0095] Step 304 involves sampling the data. In this step, a second data set may be selected from the first data set based on specified sampling rules. For example, a user can select a Y label (dependent variable) and sampling method, and set a random seed to establish the sampling rules. The sampling rules can adopt one or more sampling methods, such as random sampling, stratified sampling, or oversampling. This step facilitates in creating a representative subset of the data, which can be particularly useful when dealing with large datasets or when balancing different classes in the data.

[0096] At step 306, the process 204 applies feature derivation. This step generates derived features based on specified feature derivation rules according to the features of the second data set, obtained from the previous sampling step, to produce a third data set that includes both the original and newly derived feature values. The feature derivation process may implement a multi-criteria approach to ensure the quality and relevance of derived features during the transformation of the second dataset into the third dataset.

[0097] First, a data type constraint criterion may determine whether the derivation logic conforms to the possible derivation methods for the involved data types present in the second dataset. For example, the data types of both the source features in the second dataset and the potential derived features for the third dataset may be analyzed to determine if the set derivation logic aligns with the permissible derivation methods.

[0098] Second, a feature coverage criterion may evaluate the prevalence of feature values in the second dataset. For example, if the coverage falls below a preset threshold, the related derivation logic may be filtered out to maintain data quality in the resulting third dataset.

[0099] Third, a semantic criterion may assess the meaning of the derived features based on their source features from the second dataset and the corresponding derivation logic. This semantic analysis can be implemented using various algorithms and may be subject to manual adjustment by users if needed to ensure the third dataset contains meaningful derived features.

[0100] For example, a derived feature like "career development expectation" could be created in the third dataset based on features such as "occupation" and "age" from the second dataset. This derived feature could serve as an indicator of repayment ability or risk in a financial context. The multi-faceted approach to feature derivation enhances the system's ability to generate meaningful and predictive features in the third dataset, thereby improving the overall quality of the sample data used in subsequent modeling steps. The feature derivation process enhances the predictive power of the model by creating new, potentially more informative features in the transition from the second to the third dataset

[0101] At step 308, the process 204 encodes the data. For convenience of processing, this step may convert the third data set into encoded data based on an encoding method. The encoding method may include one-hot encoding, woe encoding, or ordinal variable encoding. The encoding transforms categorical variables of the data into a format suitable for machine learning algorithms, thereby facilitating improved predictive performance. At step 310, process 204 addresses missing values within the encoded dataset to generate a complete and consistent dataset suitable for further analysis. This imputation step may be integrated into an automated data preprocessing pipeline, allowing the system 100 to apply imputation techniques based on user specifications or predefined settings. The system 100 can fill missing values using methods such as median substitution, user-defined fixed value assignments, or deletion of incomplete records. Users may also specify custom imputation strategies as needed to align with specific risk control requirements.

[0102] Step 312 processes outliers by removing outliers from the filled data to generate normal data. Outliers can be processed in various ways, such as isolation forest detection, Z-score detection, or custom filtering. This step helps in reducing the impact of extreme values that could potentially skew the model's predictions.

[0103] Finally, at step 314, the process filters duplicates. This step may remove duplicate values from the normal data to generate the final sample data. For example, this filtering process might perform single-field or multi-field deduplication based on user selection. The filtering of duplicates ensures that each data point in the final dataset is unique, preventing any potential bias from repeated data.

[0104] This process 204 of FIG. 3 transforms the raw data into a refined sample dataset through a series of data processing steps. These steps include data selection, sampling, feature derivation, encoding, addressing missing values, processing outliers, and filtering duplicates. Each step in this process addresses specific aspects of data preparation, aiming to create a dataset suitable for use in predictive modelling for risk control scenarios.

[0105] To illustrate the versatility of the predictive model generation and optimization process, two distinct embodiments of process 206 are presented. FIG. 4 depicts Embodiment 206a, which utilizes traditional machine learning techniques, while FIG. 5 illustrates Embodiment 206b, which incorporates a model tuning framework for enhanced scalability and efficiency.

[0106] FIG. 4 is a flowchart illustrating a process 206a for generating and optimizing a predictive model, according to an embodiment of the present disclosure. This process 206a may be implemented by components of the system architecture 100 shown in FIG. 1.

[0107] As shown in FIG. 4, at step 402, the Model Generation and Optimization Module 110 presets predictive model algorithms and hyperparameter search settings. This step includes selecting one or more types of risk control model algorithms from multiple candidate types. These candidate types may include, but are not limited to, logistic regression, extreme gradient boosting, boosting machine, gradient boosting, naive Bayes, decision tree, and random forest. In examples, the presetting process also sets the model hyperparameter search settings, which may include the hyperparameter search method, number of training iterations, training duration, and hyperparameter search range. The hyperparameter search method may be selected from options such as tree-structured Parzen estimator, grid search method, random search method, simulated annealing method, naive evolutionary method, batch optimization method, and black-box optimization method. This presetting process allows for flexibility in model selection and optimization, catering to various risk control scenarios.

[0108] At step 404 the Model Generation and Optimization Module 110 optionally selects and applies feature selection. The selection process may employ methods such as L1 regularization, random forest algorithm, or indicator judgment method to select the most relevant features for the model. The feature selection may be based on one or more preset risk control model algorithm types. For instance, a first feature selection may be applied for a first preset risk control model algorithm type, and a second feature selection (or no feature selection) may be applied for a second preset risk control model algorithm. The selection and application of features selection helps in identifying informative features, potentially improving model performance and interpretability.

[0109] At step 406, the Model Generation and Optimisation Module 110 trains the predictive model. In examples, the sample data generated in the previous process (FIG. 3) is used for training. The training process may include iteratively adjusting model parameters to minimize a loss function, with the specific approach depending on the chosen algorithm. The training is guided by predetermined first model evaluation metrics, which may include KS (Kolmogorov-Smirnov statistic), Gini coefficient, ROC (Receiver Operating Characteristic) curve, AUC (Area Under the Curve), PSI (Population Stability Index), among others. These metrics help assess the model's performance during the training phase and guide the optimization process.

[0110] At step 408 the Model Tuning System 114 implements hyperparameter search. A preset hyperparameter search method may be used to find optimal hyperparameters within the specified search range. This search process may be constrained by the preset number of training iterations or training duration to control computational resources. For example, if using a grid search method, the process might try every possibility in all candidate hyperparameter selections, training a model for each combination and selecting the best-performing hyperparameters on a validation set.

[0111] At step 410, the Model Generation and Optimization Module 110 optionally applies binning. Binning operations may be set for certain algorithm types, using methods such as decision tree optimal binning or chi-squared binning. Binning can be particularly useful for certain types of models, such as logistic regression, where it can help in processing non-linear relationships and reducing the impact of outliers.

[0112] Finally, at step 412, the Model Evaluation Module 112 evaluates the trained predictive model and determines the optimal predictive model. This evaluation may be based on the predetermined first model evaluation metrics, allowing the process 206a to determine the optimal algorithm type and optimal hyperparameter values. The evaluation of the predictive model may include comparing the performance of multiple model configurations and selecting the one that best meets the specified criteria. For example, the evaluation process may include generating comprehensive reports and visualizations to aid in model interpretation and comparison. This includes the ability to generate interpretable rule combinations extracted from model results, enhancing the overall interpretability of the risk control models. This feature is particularly valuable for non-expert users who need to understand and explain model decisions

[0113] This process 206a of FIG. 4 enables automated generation and optimization of risk control models, reducing barriers to model development. It allows for efficient exploration of various model types and hyperparameters, potentially improving the performance of the resulting models. The modular nature of this process provides flexibility, allowing users to adapt the model generation and optimization approach to specific needs and computational resources.

[0114] FIG. 5 is a flowchart illustrating an alternative process 206b for generating and optimizing a predictive model including a model tuning framework, according to an embodiment of the present disclosure. This process may be implemented by components of the system architecture 100 shown in FIG. 1 , particularly the Model Generation and Optimization Module 110 and the Model Tuning System 114.

[0115] As shown in FIG. 5, at step 502, the process 206b configures the hyperparameter search space. The Model Tuning System 114 may customize the search space of hyperparameters to search for optimal settings within this space. This customization may include defining the range and possible values for each hyperparameter that will be tuned. For example, the search space might include parameters such as learning rate, number of trees for ensemble methods, or regularization strength, with specified ranges or distributions for each. The hyperparameter search space may also define relationships or constraints between hyperparameters, facilitating a structured exploration of the model's configuration space.

[0116] At step 504, the process initiates model tuning. In this step, the Model Tuning System 114 starts a machine learning framework, such as Neural Network Intelligence (NNI), an open- source AutoML toolkit designed to automate hyperparameter optimization. The initiation of model tuning sets up the framework for the subsequent steps in the optimization process. The machine learning framework employs advanced optimization algorithms, such as Bayesian optimization, genetic algorithms, or reinforcement learning, to intelligently navigate the hyperparameter search space defined in step 502.

[0117] At step 506, model hyperparameters are generated. Using the optimization methods provided by the machine learning framework, the Model Tuning System 114 may generate a set of hyperparameters to perform parameter tuning on the model. This could involve techniques such as Bayesian optimization to select promising hyperparameter configurations based on prior evaluation results. This process helps determine a set of hyperparameters that are likely to enhance model performance.

[0118] At step 508, the predictive model is trained. The Model Generation and Optimization Module 110 may use the hyperparameters generated in step 506 to train a model instance. This training process may include using a portion of the available data (training set) to adjust the model's internal parameters based on the chosen algorithm and hyperparameters.

[0119] At step 510 the performance of the trained predictive model is evaluated. This evaluation may be conducted by the Model Evaluation Module 112, using a separate portion of the data (validation set) not used in training. The evaluation metrics may include those mentioned in the disclosure, such as KS, Gini, ROC, AUG, and PSI. This evaluation step provides a quantitative measure of how well the model performs with the current set of hyperparameters.

[0120] Finally, at step 512, the process determines the next steps based on the evaluation in step 510. This decision-making step may involve comparing the current model's performance against previous iterations or predetermined thresholds. The iterative process of hyperparameter tuning continues until a predefined criterion is met. This predefined criterion may include one or more of the following conditions:

[0121] Performance Threshold: The process concludes when the predictive model achieves or exceeds a specified performance metric, indicating that further optimization yields diminishing returns.

[0122] Maximum Iterations: A limit on the number of iterations is set to prevent excessive computational resource consumption.

[0123] Convergence Condition: The tuning process terminates when improvements in performance between successive iterations fall below a defined threshold, suggesting that the model has reached optimal or near-optimal performance. Time Constraint: The process stops after a predetermined duration has elapsed, ensuring that the tuning phase aligns with project timelines.

[0124] If the predefined criterion is not met, the process loops back to step 506 to generate new hyperparameters and continue the optimization process.

[0125] This iterative process facilitates model versioning and performance tracking over time. For example, the system 100 may maintain a history of model versions, including their respective hyperparameters and performance metrics, allowing users to monitor how model performance evolves as new data becomes available or as underlying risk patterns change. This feature facilitates easy comparison between model versions and allows for rollback to previous versions if necessary.

[0126] The process described in FIG. 5 presents an iterative approach that allows for continuous refinement of the predictive model, leading to improved performance over multiple cycles. It provides a structured method for exploring various hyperparameter combinations, which contributes to finding optimal settings for the model. By utilizing machine learning frameworks like NNI and establishing clear stopping criteria, the Model Tuning System 114 efficiently optimizes the predictive model's hyperparameters, enhancing performance while managing resource utilization. By systematically evaluating different configurations at each iteration, the process aims to guide the optimization towards more effective predictive models for risk control scenarios. This approach may capture nuances in the data that might not be evident in a single training cycle. The step-by-step framework could be adapted to various types of predictive models, offering a consistent approach to model tuning across different risk control applications. While potentially reducing the manual effort required in model tuning, this method may also help in developing more robust and accurate predictive models.

[0127] FIG. 6 is a block diagram illustrating a technical architecture 600 of a computing server capable of implementing the predictive modelling and deployment processes of FIGs 2-5, according to an embodiment of the present disclosure. The technical architecture 600 represents a computer system suitable for executing one or more embodiments of the invention. While a single computing server is shown, the methods may be implemented across multiple computers in a distributed computing environment.

[0128] The technical architecture 600 includes a processor 622 (which may be referred to as a central processor unit or CPU) that is in communication with memory devices including secondary storage 624 (such as disk drives), read only memory (ROM) 626, random access memory (RAM) 628. The RAM 628 may be partitioned to efficiently process different tasks, such as data pre-processing, feature engineering, model training, and evaluation. The partitioning of RAM 628 allows for efficient processing of large-scale data processing tasks, which is particularly beneficial for complex predictive modelling operations. The processor 622 may be implemented as one or more CPU chips. The technical architecture may further comprise input / output (I / O) devices 630, and network connectivity devices 632.

[0129] The secondary storage 624 is typically comprised of one or more disk drives or tape drives and is used for non-volatile storage of data and as an over-flow data storage device if RAM 628 is not large enough to hold all working data. Secondary storage 624 may be used to store programs which are loaded into RAM 628 when such programs are selected for execution.

[0130] In this embodiment, the secondary storage 624 includes a predictive analytics component 624a comprising non-transitory instructions operative by the processor 622 to perform various operations of the predictive modelling and deployment method. Specifically, the predictive analytics component 624a may implement the functionality of the various modules illustrated in FIG. 1, including the user interface module 102, data input module 104, data pre-processing module 106, feature engineering module 108, model generation and optimisation module 110, model evaluation module 112, model tuning system 114, and model deployment module 116. This modular structure allows for flexibility in implementing various predictive modelling techniques and adapting to different types of risk control scenarios.

[0131] The ROM 626 is used to store instructions and perhaps data which are read during program execution. The secondary storage 624, the RAM 628, and / or the ROM 626 may be referred to in some contexts as computer readable storage media and / or non-transitory computer readable media.

[0132] I / O devices 630 may include printers, video monitors, liquid crystal displays (LCDs), plasma displays, touch screen displays, keyboards, keypads, switches, dials, mice, track balls, voice recognizers, card readers, paper tape readers, or other well-known input devices.

[0133] The processor 622 executes instructions, codes, computer programs, scripts which it accesses from hard disk, floppy disk, optical disk (these various disk-based systems may all be considered secondary storage 624), flash drive, ROM 626, RAM 628, or the network connectivity devices 632. While only one processor 622 is shown, multiple processors may be present. Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors.

[0134] Although the technical architecture is described with reference to a computer, it should be appreciated that the technical architecture may be formed by two or more computers in communication with each other that collaborate to perform a task. For example, but not by way of limitation, an application may be partitioned in such a way as to permit concurrent and / or parallel processing of the instructions of the application. Alternatively, the data processed by the application may be partitioned in such a way as to permit concurrent and / or parallel processing of different portions of a data set by the two or more computers. In an embodiment, virtualization software may be employed by the technical architecture 600 to provide the functionality of a number of servers that is not directly bound to the number of computers in the technical architecture 600. In an embodiment, the functionality disclosed above may be provided by executing the application and / or applications in a cloud computing environment. Cloud computing may comprise providing computing services via a network connection using dynamically scalable computing resources. A cloud computing environment may be established by an enterprise and / or may be hired on an as-needed basis from a third- party provider.

[0135] The technical architecture 600 may leverage parallel processing systems, such as CUDA, for model computation acceleration. This may include targeted compilation of the required operations into a structure that can be run by the parallel processing system. For example, the system can utilize GPU acceleration to enhance the speed and efficiency of model training and evaluation, particularly for computationally intensive tasks in risk control modelling. Additionally, the system supports containerized deployment using technologies like Docker, enabling distributed computing for risk control management with advantages of high availability, reliability, and scalability. This combination of parallel processing and containerized deployment allows the system to efficiently handle large volumes of data and complex models, making it suitable for real-time risk assessment and decision-making in various business contexts. The parallel processing capabilities are particularly beneficial for tasks such as hyperparameter tuning and model evaluation, where multiple model variations can be tested simultaneously.

[0136] By programming and / or loading executable instructions onto the technical architecture, at least one of the CPU 622, the RAM 628, and the ROM 626 are changed, transforming the technical architecture in part into a specific purpose machine or apparatus having the novel functionality taught by the present disclosure. It is fundamental to the electrical engineering and software engineering arts that functionality that can be implemented by loading executable software into a computer can be converted to a hardware implementation by well-known design rules. Such hardware implementations may include field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), which can further enhance the system's performance for specific predictive modelling and risk control tasks. Whilst the foregoing description has described exemplary embodiments, it will be understood by those skilled in the art that many variations of the embodiments can be made within the scope of the invention as defined by the claims. Moreover, features of one or more embodiments may be mixed and matched with features of one or more other embodiments.

Claims

CLAIMS1. A system for predictive modeling, comprising: a data input module configured to acquire raw data; a data pre-processing module configured to process the raw data to generate sample data; a feature engineering module configured to generate derived features using one or more specified criterion; a model generation and optimization module configured to generate and refine a predictive model using a plurality of machine learning techniques; a model evaluation module configured to assess model performance using predefined metrics; and a model tuning system configured to iteratively adjust model parameters of the predictive model based on performance feedback;2. The system of claim 1, further comprising a user interface module configured to facilitate interaction between a user and the system.

3. The system of claim 1 or 2, wherein the data pre-processing module is configured to process at least one of numerical data, categorical data, or time-series data.

4. The system of any one of claims 1 to 3, wherein the one or more specified criterion used by the feature engineering module includes at least one of a data type constraint criterion, a feature coverage criterion, or a semantic criterion.

5. The system of any one of claims 1 to 4, further comprising a model deployment module configured to deploy the predictive model using containerized distributed deployment and implement the predictive model in a production environment.

6. The system of any one of claims 1 to 5, wherein the model tuning system is configured to maintain a record of a plurality of model versions of the predictive model, including their respective performance metrics.

7. A method for predictive modeling, comprising: obtaining raw data comprising multiple feature values corresponding to various features and their respective labels; processing the raw data to generate sample data suitable for modeling, the processing including applying feature derivation to generate derived features based on specified derivation rules using one or more criteria; generating and optimizing a predictive model using the sample data; and evaluating the predictive model using predetermined evaluation metrics;8. The method of claim 7, further comprising deploying the predictive model in a production environment and wherein the one or more criteria used in the feature derivation include at least one of a data type constraint criterion, a feature coverage criterion, or a semantic criterion.

9. The method of claim 7 or 8, wherein processing the raw data further comprises at least one of: selecting data based on specified features, sampling the selected data according to specified sampling rules, encoding the selected data using a specified encoding method, identifying missing values in the selected data; processing outliers in the selected data, and filtering duplicates to generate a final sample data.

10. The method of any one of claims 7 to 9, wherein the sampling rules include at least one of random sampling, stratified sampling, or oversampling.

11. The method of any one of claims 7 to 10, wherein the encoding method is selected from one-hot encoding, weight of evidence encoding, or ordinal variable encoding.

12. The method of any one of claims 7 to 11, wherein the predetermined evaluation metrics include at least one of Kolmogorov-Smirnov statistic, Gini coefficient, receiver operating characteristic curve, area under the curve, or population stability index.

13. A method for generating a predictive model, comprising: presetting one or more types of predictive model algorithms and hyperparameter search settings; training the predictive model using sample data based on predetermined evaluation metrics; implementing hyperparameter search using a specified search method and search range; applying feature selection to identify relevant features for the predictive model; applying binning operations to the sample data; and evaluating the predictive model to determine optimal algorithm types and hyperparameter values based on the predetermined evaluation metrics.

14. The method of claim 13, wherein the hyperparameter search method is selected from tree- structured Parzen estimator, grid search, random search, simulated annealing, naive evolutionary method, batch optimization, or black-box optimization.

15. The method of claim 13 or 14, wherein the predictive model algorithms are selected from logistic regression, extreme gradient boosting, boosting machine, gradient boosting, naive Bayes, decision tree, or random forest.

16. The method of any one of claims 13 to 15, wherein the feature selection method is selected from L1 regularization, random forest algorithm, or indicator judgment method.

17. The method of any one of claims 13 to 16, wherein the binning operations are selected from decision tree optimal binning or chi-squared binning.

18. A method for tuning a predictive model, comprising: configuring a hyperparameter search space for a predictive model; initiating a model tuning process using a machine learning framework; generating model hyperparameters within the hyperparameter search space; training the predictive model using the generated hyperparameters; evaluating the performance of the trained predictive model using predefined evaluation metrics; iteratively repeating the steps of generating hyperparameters, training, and evaluating until a predefined criterion is met.

19. The method of claim 18, wherein the machine learning framework is configured to perform parameter tuning using an optimization method selected from Bayesian optimization, genetic algorithms, or reinforcement learning.

20. The method of claim 18 or 19, further comprising maintaining a record of model versions and their respective performance metrics during the tuning process.

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