1.1. What is AI and ML?

• Artificial Intelligence (AI): The simulation of human intelligence in machines programmed to perform tasks that typically require human cognition.
• Machine Learning (ML): A subset of AI that involves training algorithms to recognize patterns and make predictions based on data.

1.2. Why Use AI and ML for Fraud Detection?

• Predictive Analytics: AI and ML can analyze historical data to predict and identify potential fraudulent activities.
• Anomaly Detection: These technologies can detect unusual patterns or behaviors that may indicate fraud.
• Adaptive Learning: ML models can continuously learn and adapt to new fraud patterns, improving detection over time.

2.2. Integrating External AI and ML Models

Enhance Stripe Radar’s capabilities by integrating external AI and ML models:

• Data Collection: Collect transaction data from Stripe API for training your custom ML models.

javascript

const transactions = await stripe.charges.list({ limit: 100, });

• Model Training: Use this data to train your AI and ML models to recognize patterns specific to your business’s fraud risks.
• Tools: TensorFlow, Scikit-learn, or Azure ML can be used for model development.
• Integration: Deploy your models to analyze transaction data in real-time and integrate them with Stripe via API calls.

javascript

const prediction = await customModel.predict({ transaction_data: { amount: 2000, currency: 'usd', customer_id: 'cus_123', }, });

2.3. Real-Time Monitoring and Response

• Automated Alerts: Set up automated alerts for suspicious transactions identified by your AI and ML models.

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if (prediction.score < 0.5) { // Trigger alert or review process }

• Adaptive Thresholds: Adjust fraud detection thresholds dynamically based on real-time analysis and emerging trends.