⚙️ Key Technique
DAC is leveraging Generative AI, including large language models like GPT and LLaMA, to transform Affiliate Marketing in the dynamic world of e-commerce and social networks. Using AI-powered language models to create high-quality, contextually relevant content is a game-changer in the digital marketing landscape.
Incorporating AI into affiliate marketing is no longer an option; it's a necessity to thrive in the competitive digital landscape. The benefits for DAC Citizens include:
Personalization: Enabling personalized content creation for Requesters and Affiliators that resonates with individual users, boosting engagement and conversions. Recommendations and messages are tailored to user preferences and behaviors.
Efficiency: AI-driven content generation significantly reduces the time and effort required for task materials, enhancing campaign operations' efficiency.
Data-Driven Insights: Our AI generates valuable insights into user behaviors and triggers for their actions, fine-tuning activities and optimizing campaigns.
Competitive Edge: Embracing AI technologies gives DAC a competitive advantage. Offering high-quality, AI-generated content becomes a unique selling point that attracts and retains end-users.
Sentiment Analysis: Incorporating sentiment analysis helps gauge user emotions and reactions, allowing partners to fine-tune content and offers to match user sentiments, creating emotionally resonant content.
In summary, DACs' innovative use of Generative AI revolutionizes affiliate marketing, offering personalization, efficiency, data-driven insights, a competitive edge, and sentiment analysis to enhance our DAC Citizen user experience and marketing campaign effectiveness.
5. The Bot Mitigation and Anti-fraud System (BMAS)
The BMAS system is used not only to stop malicious bots and prevent fraud threats before they impact our ecosystem, but also to detect fraudulent activity. In particular, to detect fraud, there are several techniques that we are implementing:
Calculate statistical parameters
Match data
Perform Logistic Regression analysis
Use probability models and distributions
Mine data to classify, segment, and cluster data to find associations and rules signaling patterns of malicious activities
Detect fraud with heuristic rules, machine learning, and thresholds
The BMAS system will need data to get started. The more data we feed into the system to start with, the more accurate the results will be. Therefore, simulated fraud data is also fed into the system to ensure the volume, quality, and diversity in the analysis process.
The following information describes our major components in BMAS system:
Feeding the input data to the BMAS system:The data we collect, such as IP Address, Browsers, Device Type, Proxy (if used), transaction on chain, campaign identifier number, types of requests, etc., is used to build “digital avatars” or fingerprints. For the quest that needs a Sheriff to verify the action, the data is gathered to provide input to the scoring function.
Rule generation: Before we have ML/AI, using heuristic rules is one of the most efficient ways. The are two main types of rules that we use in the BMAS system:
Single-parameter rules, also known as heuristic rules.
Complex rules, including multiple parameters. We can adjust accuracy thresholds to tighten or loosen triggering conditions. The historical data will be used to help us create a confusion matrix based on collected data over the selected time frame and highlight the estimated accuracy rate of those rules.
Fraud scoring and analytics: The BMAS system leverages machine learning models to assign risk scores to activities or user accounts based on various factors, such as transaction, location, frequency, and past behavior. Higher risk scores indicate a higher likelihood of fraud, enabling us to perform further investigation. Fraudulent actors often collaborate and form networks to carry out their activities. Another machine learning technique we implemented in the BMAS system is semantic graph analysis to help uncover these networks by analyzing relationships between entities (such as users, wallets, transactions on chain, etc.) and identifying unusual connections or clusters.
Risk threshold settings: Finding the optimal risk threshold requires conducting data analysis rooted in the principles of “precision vs. recall.” Setting the right limits for allow/review/prevent thresholds depends on these model evaluation metrics. It entails a balancing act involving:
True positives (the number of fraudsters successfully blocked)
False positives (the number of legitimate individuals mistakenly blocked)
False negatives (the number of fraudsters inadvertently allowed)
Navigating this intricate balance is crucial for effective risk management in fraud detection and prevention. The BMAS system leverages LLM to understand the context and requirements from the natural language of administrators or business partners to suggest the right threshold settings.
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