AI4ALL Amsterdam Circle: Forecast Smarter, Not Harder

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Join Lorena Poenaru-Olaru, Data Scientist at ING, to explore a smarter way to maintain machine-learning forecasting models. Many organizations spend unnecessary resources constantly updating their models - but often, it’s not needed.

Through a real ING case study, you’ll learn how early detection of data shifts can reduce costs, improve responsiveness, and boost confidence in your forecasts.

💡 What you’ll gain:
• When forecasting models actually need updating
• How to detect shifts in data early
• How smarter monitoring saves time and resources
• ING’s approach to maintaining forecasting models
• Practical ideas to improve efficiency and model performance

✨ About the speaker:
Lorena Poenaru-Olaru is a Data Scientist in Real Estate Finance Business Banking at ING. She completed her PhD within the AI4Fintech research lab (TU Delft & ING Netherlands), focusing on monitoring and maintaining ML systems throughout their lifecycle. Lorena is passionate about building innovative ML solutions and ensuring their reliability from development to production.