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OUR SPEAKERS

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Tal Laron is a data science manager at Palo Alto Networks with deep experience in machine learning, cybersecurity, and fraud prevention (ex. Riskified). At her current role, she manages a team focused on building robust, high-precision models to detect malicious activity at scale. Tal holds a B.Sc in statistics & economics from the Tel Aviv University and a master’s degree in financial economics, a global program from the Reichman University. As a former gymnast she enjoys sports and sporting events.

Tal Laron

ML Research Manager, Palo Alto Networks
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English, Hebrew
Languages:
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Location:
Herzlyia , Israel
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Can also give an online talk/webinar
Paid only. Contact speaker for pricing!

MY TALKS

Build Your Own Lego Model: Connecting Historical Data to Improve Models’ Accuracy

Data / AI / ML, Leadership

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My 5 year old son is crazy about Lego. He taught me there are multiple ways to connect two Lego bricks. This made me realize that this approach can be applied to my work as a data scientist
Using Lego, I will explore how different data points can be connected and combined to build a robust model for improving fraud detection in e-commerce using machine learning, which is what we do at Riskified.
For example, addresses can be connected and combined by geographical distance or by textual similarity, which gives us a hybrid approach for improving our models.
By the end of this talk, you will gain an understanding of how hybrid techniques can improve the performance of predictive models and build your own hybrid lego model.

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Leveraging Sub-Population Splits for Accurate Fraud Predictive Models

Data / AI / ML, Soft Skills, Women in Tech, Diversity and Inclusion

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In the fraud prevention industry, identifying fraudulent activities can be a complex and challenging task. Accurate predictive models are crucial in order to prevent fraudulent activities before they occur. Splitting to sub-population is a powerful technique that can improve the accuracy of predictive models by segmenting data into meaningful groups based on common characteristics.
In this session, we explored the fundamental principles of data splitting and its practical application in real world scenarios. We discussed the benefits of data segmentation in fraud prevention and develop a comprehensive understanding of how it can help refine predictive models, minimize false positives, and optimize business value in the industry.
Furthermore, we learned about the best practices for executing data splitting strategies and how to seamlessly integrate these strategies into existing fraud prevention systems.

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Build Your Own Lego Model: Connecting Historical Data to Improve Models’ Accuracy

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Leveraging Sub-Population Splits for Accurate Fraud Predictive Models

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