Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
Data Science
Towards Data Science·Aug 27·5 min read
I Trained Six Models for Fraud Detection, and the Best One Isn't in Production
TL;DR
After training six different models for fraud detection, the most effective one was ultimately not deployed in production due to practical considerations. This highlights the disconnect between model performance metrics and real-world application.
✦ Why It Matters
Evaluate not just model performance but also practical deployment factors when selecting machine learning solutions.
Key Takeaways
- Train multiple models and log their performance for future reference.
- Incorporate human review processes in automated systems.
- Use techniques like SMOTE to address class imbalance in datasets.
- Regularly reassess production models against newer, better-performing alternatives.
How It Works
NairaShield merges two datasets with different schemas, validating them before training. It uses features common to both datasets, such as transaction amount and one-hot encodings, to create a unified model.
The system flags transactions based on a confidence threshold, routing them for human review if necessary.