Predictive Model for Credit Card Fraud Detection

Requirements Clarification & Assessment

1. Objective Definition

   • Clearly define the goal: Predict if a transaction is fraudulent or not.
   • Determine the critical features influencing fraud detection, such as transaction amount, time, location, and user behavior.

2. Data Availability & Understanding

   • Assess the availability and quality of transaction data.
   • Identify any gaps or missing data that need to be addressed.
   • Understand the distribution of fraudulent vs. genuine transactions to assess data imbalance.

3. Stakeholder Expectations

   • Engage with stakeholders to understand the acceptable level of false positives and false negatives.
   • Determine the business impact of fraud detection accuracy on customer experience and financial loss.

4. Regulatory and Compliance Requirements

   • Identify any legal or compliance requirements related to handling financial data.
   • Ensure data privacy and security measures are in place.

5. Technical Constraints

   • Evaluate the existing infrastructure and technology stack for model deployment and monitoring.
   • Consider computational resources available for model training and prediction.