Fraud Detection with Machine Learning

Overview

• Machine Learning (ML) is a collection of AI algorithms trained with historical data.
• Used to suggest risk rules to block or allow user actions (e.g., suspicious logins, identity theft, fraudulent transactions).

Key Concepts

Artificial Intelligence (AI)

• Machines simulate human thinking.

Machine Learning (ML)

• Subset of AI.
• Allows machines to learn from data without reprogramming.

Advantages of ML in Fraud Detection

• Efficiency: Quickly processes large data sets to identify suspicious patterns.
• Speed: Faster detection compared to human review.
• Prediction Accuracy: Improves with more data.
• Cost-Effectiveness: Reduces need for more human risk ops agents.
• Reliability: Consistent performance; unaffected by human limitations like fatigue.

Practical Outcomes

• Faster and more efficient fraud detection.
• Reduced manual review time.
• Better predictions with larger datasets.
• A more cost-effective solution compared to human monitoring.
• Algorithms don't get tired, unlike human fraud managers.

Performance Metrics

• According to a whitepaper by the University of Jakarta, ML algorithms achieved up to 96% accuracy in reducing fraud for e-commerce businesses.