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Title & Speakers	Event
Event Payment Routing Optimization & Next-Gen Data Notebooks 2025-06-19
Let’s completely rethink the notebook 2025-06-19 · 19:45 Jupyter notebooks have been around for a while now and are an approachable starting point for anyone interested to explore data and programming. It's done a lot of good, but it was never really designed to be 'the' Python notebook. It's always wanted to support multiple languages and this is reflected in many of the design decisions. And that makes you wonder, what could one do if we really did do just that? What if we ignored other languages and really only cared about Python and data tools? Would the notebook be different? The goal of this talk is to demonstrate this idea by talking about widgets, reactive Python, modern data tooling and also about the freedom to rethink your tools. There will also be demos. Lots. Of. Demos. Python
Multi-Objective Optimization in Payment Routing 2025-06-19 · 18:30 Machine learning (ML) models in production often start with a single objective, such as maximizing conversion rate in the payment industry. However, real-world business contexts are often more nuanced where other aspects relevant to a transaction, such as transaction cost or fraud risk, come into play. These objectives can be inherently conflicting: while optimizing for authorization may drive more revenue, it could also lead to higher costs or increased risk exposure. Addressing such trade-offs necessitates the consideration of multi-objective optimization (MOO), while key information in the payment context plays a role in determining which objective should get more weight when considering the trade-off. In this talk we will share how we (Optimize ML team at Adyen) use the contextualized scalarization approach to improve our Intelligent Payment Routing product with a focus on conversion rate and transaction cost optimization. AI/ML

Jupyter notebooks have been around for a while now and are an approachable starting point for anyone interested to explore data and programming. It's done a lot of good, but it was never really designed to be 'the' Python notebook. It's always wanted to support multiple languages and this is reflected in many of the design decisions. And that makes you wonder, what could one do if we really did do just that? What if we ignored other languages and really only cared about Python and data tools? Would the notebook be different?

The goal of this talk is to demonstrate this idea by talking about widgets, reactive Python, modern data tooling and also about the freedom to rethink your tools. There will also be demos. Lots. Of. Demos.

Machine learning (ML) models in production often start with a single objective, such as maximizing conversion rate in the payment industry. However, real-world business contexts are often more nuanced where other aspects relevant to a transaction, such as transaction cost or fraud risk, come into play. These objectives can be inherently conflicting: while optimizing for authorization may drive more revenue, it could also lead to higher costs or increased risk exposure.

Addressing such trade-offs necessitates the consideration of multi-objective optimization (MOO), while key information in the payment context plays a role in determining which objective should get more weight when considering the trade-off. In this talk we will share how we (Optimize ML team at Adyen) use the contextualized scalarization approach to improve our Intelligent Payment Routing product with a focus on conversion rate and transaction cost optimization.

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