Developing a Fraud Detection Model for Credit Card Transactions

Requirements Clarification & Assessment

1. Objective Understanding:

   • Develop a fraud detection model for a credit card company to identify fraudulent transactions from a dataset containing 600,000 credit card transactions.
   • Fraud is defined as unauthorized use of a credit card.

2. Business Context:

   • Protect customers by preventing unauthorized transactions.
   • Improve customer service and retention.
   • Reduce financial losses and resource allocation on fraud claims.

3. Data Characteristics:

   • Dataset includes 600,000 transactions.
   • Likely imbalance with fewer fraudulent transactions.
   • Data may include features like transaction amount, location, merchant category, etc.
   • Determine if data is labeled (fraudulent vs. non-fraudulent).

4. Technical Considerations:

   • Need for real-time or near-real-time detection (latency requirements).
   • Model interpretability for customer communication and regulatory compliance.
   • Handling of missing data and feature engineering.

5. Ethical and Legal Considerations:

   • Ensure ethical use of data, especially concerning customer privacy.
   • Compliance with legal standards for fraud detection and reporting.