Fraud Detection Model Using Amazon Fraud Detector

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🔹 First Principles

What?

Amazon Fraud Detector is a fully managed ML service that detects fraudulent activities like fake accounts and payment fraud. It automates model training, deployment, and real-time fraud detection using your data for high accuracy.

Why?

Fraud costs businesses billions annually. Traditional rule-based fraud detection struggles against evolving fraud tactics. Machine Learning (ML) adapts dynamically, reducing false positives and improving detection accuracy. Amazon Fraud Detector simplifies ML adoption with no infrastructure management and a pay-as-you-go pricing model.

How?

1. Store Data: Upload labeled fraud data (email, IP, timestamps) to Amazon S3.
2. Define Event: Set up fraud-related variables in Amazon Fraud Detector.
3. Train Model: Let AWS automate model building and optimization.
4. Deploy Detector: Combine ML predictions with fraud rules for real-time decisions.
5. Monitor & Optimize: Adjust thresholds and refine fraud detection over time.
6. Cleanup: Remove unused resources to save costs.

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1️⃣ Introduction

In this project, we implemented a fraud detection model using Amazon Fraud Detector, a fully managed ML service that identifies potentially fraudulent transactions such as fake account registrations and payment fraud.

Architecture

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2️⃣ Objectives

The primary goals of this project were:

1. Understand Amazon Fraud Detector Components – Learn how detectors, models, and rules function.
2. Prepare Training Data – Store labeled dataset in Amazon S3.
3. Train a Fraud Detection Model – Use historical event data for training.
4. Deploy and Test the Model – Evaluate predictions based on predefined fraud scores.
5. Publish and Invoke the Fraud Detector – Integrate it into a decision-making workflow.

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3️⃣ Implementation Steps

Step 1: Learning Amazon Fraud Detector Components

• Amazon Fraud Detector consists of:
  • Detectors: Categorization engines that apply rules based on ML model outputs.
  • Models: ML-driven fraud detection trained on user-provided data.

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Step 2: Data Preparation (Amazon S3)

• Created an S3 bucket for training data storage.
• Uploaded registration_data_20K_minimum.csv, containing:
  • Email address
  • IP address
  • Timestamp
  • Fraud label (fraud or legit)

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Step 3: Event & Variable Configuration

• Defined an event type: "registration", representing user account creation.
• Created entities & labels:
  • customer entity type.
  • fraud and legit labels matching dataset classifications.
• Defined input variables:
  • email_address → Type: Email Address
  • ip_address → Type: IP Address
• Configured IAM Role to allow Amazon Fraud Detector to access S3 training data.

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Step 4: Model Training & Deployment

• Created an ML model (Online Fraud Insights) and linked it to the "registration" event.
• Trained the model on historical fraud data.
• Evaluated model performance:
  • Thresholds selected:
    • High risk: Score ≥ 900
    • Medium risk: 700 < Score < 900
    • Low risk: Score ≤ 700
• Deployed model version 1.0 to Amazon Fraud Detector.

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Step 5: Fraud Detector Rules & Publishing

• Defined risk-based outcomes:
  • high_risk: Fraud detected.
  • medium_risk: Requires manual review.
  • low_risk: Legitimate.
• Created fraud detection rules:
  • auto-fraud-rule: $model_insightscore >= 900 → high_risk
  • review-rule: 700 < $model_insightscore < 900 → medium_risk
  • auto-legit-rule: $model_insightscore <= 700 → low_risk
• Published the detector to make it active for real-time fraud detection.

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Step 6: Testing & API Invocation

Testing in AWS Console

• Ran test cases using historical data:
  • Fraudulent event correctly predicted as high risk.
  • Legitimate event predicted as low risk.

Invoking Fraud Detector Using Boto3

Executed the following Python script to check real-time fraud detection:

import boto3, uuid, json

client = boto3.client('frauddetector', region_name='us-east-2')

response = client.get_event_prediction(
    detectorId="detector-getting-started",
    eventId=str(uuid.uuid4()),
    eventTypeName="registration",
    eventTimestamp="2024-02-28T14:00:00Z",
    entities=[{"entityType": "customer", "entityId": str(uuid.uuid4())}],
    eventVariables={
        "email_address": "fakeuser@example.org",
        "ip_address": "192.168.1.1"
    }
)

print('Predicted outcome:', json.dumps(response['ruleResults'][0]['outcomes']))

✅ Output Example:

"Predicted outcome: ['low_risk']"

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7️⃣ IAM Roles & Security Implementation

• IAM Role Created: AmazonFraudDetector-DataAccessRole-XXX
• IAM Permissions Used:

  {
      "Effect": "Allow",
      "Action": [
          "frauddetector:PutDetector",
          "frauddetector:GetDetector",
          "frauddetector:GetEventPrediction",
          "s3:GetObject"
      ],
      "Resource": "*"
  }

• Security Considerations:
  • Used AWS SSO for authentication instead of static IAM credentials.
  • Restricted permissions to Fraud Detector and S3 access only.

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8️⃣ Cleanup & Cost Considerations

Since Amazon Fraud Detector charges per request, we deleted all resources to avoid unnecessary costs:

1. Deleted S3 bucket (fraud-detector-getting-started-1).
2. Removed IAM Role & Policies (AmazonFraudDetector-DataAccessRole-XXX).
3. Deleted Fraud Detector, Rules, and Model from AWS console.

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9️⃣ Key Takeaways & Lessons Learned

• ✅ Amazon Fraud Detector automates fraud detection with machine learning.
• ✅ Model performance can be fine-tuned by adjusting fraud score thresholds.
• ✅ AWS IAM best practices should be followed by implementing least privilege access.
• ✅ SSO-based authentication ensures security when integrating with AWS APIs.

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🔹 Final Thoughts & Next Steps

• Future Improvements:
  • Implement real-time fraud alerts via AWS Lambda.
  • Integrate with Amazon EventBridge for automated response actions.
• Next Learning Steps:
  • Explore Amazon Fraud Detector Notebooks for custom model enhancements.
  • Experiment with AWS SageMaker for advanced ML-based fraud detection.

🚀 This project successfully demonstrated how to build, train, and deploy a real-world fraud detection model on AWS! 🎯

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