Risk control is an indispensable part of digital marketing, which involves user credit, transaction security, platform compliance, etc., and is crucial for e-commerce, advertising and marketing, and user growth. Traditional risk control methods often rely on techniques such as manual rules, statistical analysis, and machine learning, but with the increase in data volume and the complexity of business scenarios, the limitations of these methods are becoming more and more obvious. Large AI models, such as GPT-3 and BERT, are major breakthroughs in the field of artificial intelligence in recent years, which can use massive data and powerful computing power to learn the underlying laws and knowledge in the data, so as to show amazing results in various tasks. From the perspective of a product manager, this article will introduce the steps of how to apply the AI model to implement the risk control filtering model, including data collection and preprocessing, model construction, model evaluation and optimization, model deployment and application, etc., as well as the roles and responsibilities of product managers and operators in these steps. This article will also combine some examples and schematic diagrams to show the application effects and advantages of AI large models in the field of risk control, as well as the challenges and risks faced. This article aims to help product managers and operators understand and master the application methods and value of AI models in risk control business, so as to improve the efficiency and security of digital marketing.
Risk control, i.e., risk control, refers to the identification, assessment, prevention and treatment of various possible risks through a series of means and measures in digital marketing, so as to protect the interests and rights of users, platforms and partners. Risk control involves many aspects, such as user credit, transaction security, platform compliance, anti-fraud, anti-money laundering, anti-spam, etc., which are crucial for e-commerce, advertising and marketing, and user growth. For example, on e-commerce platforms, risk control needs to identify and intercept fraudulent transactions, order swiping, reviews, malicious refunds, etc., to protect the rights and interests of merchants and consumers. On advertising and marketing platforms, risk control needs to identify and filter invalid clicks, cheating traffic, illegal content, etc., to ensure the return on investment of advertisers and the brand image of the platform. On the user growth platform, risk control needs to identify and block bots, water armies, black cards, etc., to ensure the authenticity and activity of users.
Traditional risk control methods often rely on technologies such as manual rules, statistical analysis, and machine learning, which can meet the needs of risk control to a certain extent, but with the increase of data volume and the complexity of business scenarios, the limitations of these methods are becoming more and more obvious. For example, manual rules need to be manually written and maintained, which is not only time-consuming and labor-intensive, but also prone to vulnerabilities and manslaughter, and difficult to adapt to changing risk characteristics. Statistical analysis requires manual selection and adjustment of indicators and thresholds, which is not only highly subjective, but also has low accuracy, and it is difficult to capture subtle risk signals. Machine learning requires manual annotation and cleaning of data, which is not only costly, but also of poor quality, making it difficult to ensure the generalization ability and robustness of the model.
Large AI models, such as GPT-3 and BERT, are major breakthroughs in the field of artificial intelligence in recent years, which can use massive data and powerful computing power to learn the underlying laws and knowledge in the data, so as to show amazing results in various tasks. The large artificial intelligence model has the following characteristics:
Data-driven: Artificial intelligence models do not need to manually write rules or label data, but learn and extract features and knowledge from large-scale data in an automated way, so as to realize the value transformation of data.
Versatility: Large AI models do not need to be designed or trained separately for each task or domain, but can be pre-trained and fine-tuned to achieve cross-task and cross-domain migration and adaptation of models.
Intelligence: Large AI models can not only complete simple tasks such as classification or regression, but also complete complex tasks such as generation, reasoning, and dialogue, and even show a certain degree of creativity and understanding.
Artificial intelligence models have broad application prospects and potential in the field of risk control, which can effectively solve the limitations of traditional risk control methods, improve the efficiency and accuracy of risk control, and reduce the cost and risk of risk control. For example, the AI model can automatically identify and extract risk characteristics and knowledge from multi-dimensional and multi-channel data, so as to achieve comprehensive and in-depth analysis and evaluation of users, transactions, content, etc. The AI model can flexibly adjust and optimize the structure and parameters of the model according to different business scenarios and risk control objectives, so as to achieve accurate and effective interception and processing of risks. The AI model can continuously update and improve the performance and capabilities of the model according to the changes and development of risks, so as to achieve dynamic and proactive monitoring and early warning of risks.
From the perspective of a product manager, this article will introduce the steps of how to apply the AI model to implement the risk control filtering model, including data collection and preprocessing, model construction, model evaluation and optimization, model deployment and application, etc., as well as the roles and responsibilities of product managers and operators in these steps. This article will also combine some examples and schematic diagrams to show the application effects and advantages of AI large models in the field of risk control, as well as the challenges and risks faced. This article aims to help product managers and operators understand and master the application methods and value of AI models in risk control business, so as to improve the efficiency and security of digital marketing.
Data is the foundation and core of AI models, and there is no model without data. Therefore, data collection and preprocessing is the first and most important step in the implementation of the risk control filtering model. In this step, product managers and operations staff need to complete the following tasks:
Data: Determine the scope and scope of the data, including internal and external data, as well as the type and format of the data. For example, for the risk control and filtering model of the e-commerce platform, it may be necessary to collect data such as users' basic information, behavior trajectories, transaction records, and evaluation feedback, as well as data such as product attributes, sales, and inventory, as well as market dynamics, competition, and policies.
Data quality: Check the quality and completeness of data, including the accuracy, consistency, timeliness, and reliability of data. For example, for the risk control and filtering model of an advertising and marketing platform, you may need to check whether the data is missing, duplicated, wrong, or abnormal, and whether the data conforms to business logic and specifications.
Data cleaning: Cleans and processes data, including data filtering, deduplication, correction, filling, and standardization. For example, for the risk control and filtering model of a user growth platform, it may be necessary to filter the data, remove irrelevant or invalid data, such as bots, bots, and black cards, and correct the data, fill in the missing values, and standardize the data format.
Data analysis: Analyze and explore data, including data description, visualization, statistics, clustering, association, etc. For example, for the risk control and filtering model of an e-commerce platform, it may be necessary to describe the data, understand the distribution, characteristics, and trends of the data, visualize the data, display the charts, images, and maps of the data, and collect statistics on the data, calculate the mean, variance, and correlation of the data, and cluster the data, discover the categories, patterns, and anomalies of the data, and correlate the data, and mine the causes, effects, rules, and knowledge of the data.
The roles and responsibilities of product managers and operations personnel in the process of data collection and pre-processing are as follows:
Product Manager: The product manager is the leader and coordinator of data collection and pre-processing, and they need to develop data collection and pre-processing strategies and plans based on business needs and goals, as well as assign and supervise the tasks and progress of data collection and pre-processing. Product managers also need to communicate and collaborate with other roles such as data engineers, data analysts, and data scientists to ensure the quality and efficiency of data collection and preprocessing.
Operators: Operators are the executors and participants of data collection and preprocessing, and they need to complete specific operations and tasks of data collection and preprocessing according to the guidance and requirements of the product manager, such as data collection, import, export, storage, conversion, verification, cleaning, analysis, etc. Operations staff also need to provide feedback and debriefing with product managers and other roles to address data collection and pre-processing issues and pain points in a timely manner.
Data collection and preprocessing is the basis for the AI large model to realize the risk control and filtering model, which determines the input and output of the model, as well as the performance and effect of the model. Therefore, product managers and operations personnel need to pay close attention to data collection and pre-processing to ensure the quality and value of data.
Model construction is the second and most core step in the realization of the risk control and filtering model of the AI large model. In this step, product managers and operations staff need to complete the following tasks:
Model selection: Select an appropriate AI model as the basis for the risk control filtering model, including the type, structure, and parameters of the model. For example, for the risk control filtering model of an e-commerce platform, GPT-3 may need to be selected as the basic model, because GPT-3 is a generative pre-trained language model based on the self-attention mechanism, which can process multiple types of data, such as text, images, audio, etc., and generate multiple types of outputs, such as classification, regression, generation, dialogue, etc.
Model fine-tuning: Fine-tune the selected AI model to adapt it to the specific tasks and domains of the risk control filtering model, including data input, output, labeling, etc. For example, for the risk control and filtering model of an advertising and marketing platform, GPT-3 may need to be fine-tuned so that it can receive data such as content, clicks, and impressions of advertisements as inputs, as well as output labels such as the effectiveness, degree of cheating, and degree of violation of advertisements.
Model integration: Integrate multiple large AI models to enable them to collaborate and complement each other, and improve the performance and effect of risk control filtering models, including model fusion, fusion, and integration. For example, for the risk control and filtering model of the user growth platform, it may be necessary to integrate multiple large AI models, such as GPT-3, BERT, XLNet, etc., so that they can evaluate the authenticity and activity of users from different angles and levels.
The roles and responsibilities of product managers and operations personnel in the process of model building are as follows:
Product Manager: The product manager is the guide and decision-maker of model building, and they need to determine the strategy and scenario of model building, as well as evaluate and select the effects and results of model construction according to business needs and goals. Product managers also need to communicate and collaborate with other roles such as data scientists, algorithm engineers, software engineers, etc., to ensure the quality and efficiency of model building.
Operators: Operators are supporters and participants of model building, and they need to assist in specific operations and tasks of model building, such as data input, output, labeling, etc., as well as model testing, debugging, and optimization, according to the guidance and requirements of the product manager. Operations staff also need to provide feedback and debriefing with product managers and other roles to address model building issues and pain points in a timely manner.
Model construction is the core of the AI large model to implement the risk control filtering model, which determines the function and capability of the model, as well as the performance and effect of the model. Therefore, product managers and operations personnel need to attach great importance to the work of model construction to ensure the applicability and superiority of the model.
Model evaluation and optimization is the third and most critical step for the AI large model to realize the risk control and filtering model. In this step, product managers and operations staff need to complete the following tasks:
Model metrics: Determine the metrics and criteria for model evaluation, including the accuracy, stability, interpretability, scalability, etc. of the model. For example, for the risk control filtering model of an e-commerce platform, it may be necessary to determine the accuracy indicators of the model, such as accuracy, recall, and F1 value, as well as the stability indicators of the model, such as variance, bias, overfitting, and underfitting, as well as the interpretability indicators of the model, such as feature importance, impact factors, sensitivity analysis, etc., as well as the scalability indicators of the model, such as model size, training time, and inference time.
Model testing: testing and validating models, including model training, testing, and validation, as well as model comparison, analysis, and evaluation. For example, for the risk control filtering model of an advertising and marketing platform, it may be necessary to train the model, use the training set data to update and optimize the parameters of the model, test the model, use the test set data to evaluate and test the performance of the model, and validate the model, use the validation set data to test the generalization ability and robustness of the model, and compare the models, and use different artificial intelligence large models or traditional risk control methods as benchmarks to compare the effects and advantages of the models. As well as the analysis of the model, the use of different model indicators and standards, the analysis of the advantages and disadvantages of the model and the improvement points, as well as the evaluation of the model, the use of a comprehensive model evaluation system, the overall evaluation and scoring of the model.
Model optimization: Optimize and improve the model, including parameter tuning, pruning, distillation, and enhancement of the model. For example, for the risk control filtering model of the user growth platform, it may be necessary to tune the parameters of the model, use grid search, Bayesian optimization, reinforcement learning and other methods to find the optimal model parameters and hyperparameters to improve the performance and effect of the model, prune the model, use regularization, sparsity, quantization and other methods to reduce the redundancy and complexity of the model to improve the stability and scalability of the model, and distill the model using knowledge distillation, model distillation, data distillation and other methods. The knowledge and capabilities of the large model are transferred to the small model to improve the interpretability and deployability of the model, and the model is enhanced, and the diversity and difficulty of the data and tasks of the model are increased by using methods such as data augmentation, model augmentation, and task augmentation, so as to improve the generalization ability and robustness of the model.
The roles and responsibilities of product managers and operations personnel in the process of model evaluation and optimization are as follows:
Product manager: The product manager is the supervisor and evaluator of model evaluation and optimization, and they need to determine the indicators and standards of model evaluation and optimization according to business needs and goals, as well as supervise and evaluate the effect and results of model evaluation and optimization. Product managers also need to communicate and collaborate with other roles such as data scientists, algorithm engineers, software engineers, etc., to ensure the quality and efficiency of model evaluation and optimization.
Operators: Operators are the executors and participants of model evaluation and optimization, and they need to assist in the specific operations and tasks of model evaluation and optimization according to the guidance and requirements of the product manager, such as model testing, verification, comparison, analysis, evaluation, etc., as well as model tuning, pruning, distillation, enhancement, etc. Operations personnel also need to provide feedback and debriefing with product managers and other roles to address issues and challenges in model evaluation and optimization in a timely manner.
Model evaluation and optimization is the key to the realization of the risk control and filtering model of the AI large model, which determines the optimization direction and improvement space of the model, as well as the final effect and value of the model. Therefore, product managers and operators need to attach great importance to model evaluation and optimization to ensure the completeness and optimization of the model.
Model deployment and application is the fourth and final step in the implementation of the risk control and filtering model of the AI large model. In this step, product managers and operations staff need to complete the following tasks:
Model encapsulation: Encapsulates the model into an invokable interface or service, including the input, output, format, and protocol of the model. For example, for the risk control filtering model of an e-commerce platform, it may be necessary to encapsulate the model as a RESTful API, so that it can receive data in JSON format as input, return data in JSON format as output, and use the HTTP protocol for communication and transmission.
Model deployment: Deploy the model to an appropriate platform or device, including the model's environment, configuration, and resources. For example, for the risk control and filtering model of an advertising and marketing platform, it may be necessary to deploy the model to the cloud or edge to adapt to different network and computing conditions, configure the parameters, logs, and monitoring of the model, and allocate resources such as memory, CPU, and GPU of the model.
Model application: Apply the model to actual business scenarios and processes, including model triggering, execution, and feedback. For example, for the risk control and filtering model of a user growth platform, it may be necessary to apply the model to scenarios and processes such as user registration, login, activities, and recommendations, so that it can trigger the running and output of the model based on different users and events, as well as implement the results and recommendations of the model, such as interception, warnings, prompts, and rewards, and collect feedback and evaluation of the model, such as user satisfaction, business effectiveness, and model performance.
In the process of model deployment and application, the roles and responsibilities of product managers and operations personnel are as follows:
Product Manager: A product manager is the planner and manager of model deployment and application, and they need to formulate strategy and plan for model deployment and application based on business needs and goals, as well as manage and supervise the tasks and progress of model deployment and application. Product managers also need to communicate and collaborate with other roles such as software engineers, test engineers, and operations engineers to ensure the quality and efficiency of model deployment and application.
Operators: Operators are the executors and participants of model deployment and application, and they need to complete specific operations and tasks of model deployment and application according to the guidance and requirements of the product manager, such as model packaging, deployment, application, etc., as well as model testing, debugging, and optimization. Operations personnel also need to provide feedback and reporting to product managers and other roles to address issues and difficulties in model deployment and application in a timely manner.
Model deployment and application is the ultimate goal of the AI large model to realize the risk control and filtering model, which determines the practicability and value of the model, as well as the influence and contribution of the model. Therefore, product managers and operators need to attach great importance to the deployment and application of models to ensure the implementation and application of models.
From the perspective of a product manager, this article introduces the steps of how to apply the AI model to implement the risk control filtering model, including data collection and preprocessing, model construction, model evaluation and optimization, model deployment and application, etc., as well as the roles and responsibilities of product managers and operators in these steps. This paper also combines some examples and schematic diagrams to show the application effects and advantages of large AI models in the field of risk control, as well as the challenges and risks. This article aims to help product managers and operators understand and master the application methods and value of AI models in risk control business, so as to improve the efficiency and security of digital marketing.
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