Data Science in Banking: How Credit Card Fraud Detection Works
One of the most critical applications of data sciene in Banking is credit card fraud detection. Striking the right balance between catching fraudulent activity and avoiding unnecessary disruptions to legitimate customers is a major challenge. In this article, we'll explore a common data science interview scenario faced in banking, delve into the problems with traditional rule-based fraud detection, and walk through effective machine learning solutions to reduce false positives while maintaining fraud detection accuracy.
Imagine you’re a data scientist at a retail bank. The bank’s current fraud detection system is simple: it uses rules like “flag any transaction above $500” or “flag any transaction if there’s a sudden location change within 2 hours.” While this approach does catch many fraudulent transactions, it also flags 15% of all legitimate transactions as suspicious, resulting in a flood of customer complaints.
Your manager tasks you to reduce these false positives (legitimate transactions incorrectly flagged as fraud) but without letting actual fraud slip through. What do you do next?
