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Keren worked as a product analyst at Intel before transitioning to the Anti-Fraud field.
In her previous role she managed Outbrain’s Anti-Fraud domain. She is currently the Head of Anti-Fraud at MoonActive.
Keren has a BSc in Industrial Engineering from Tel Aviv University and an MSc in Computer Science from the Interdisciplinary Center Herzliya.
Keren worked as a product analyst at Intel before transitioning to the Anti-Fraud field.
In her previous role she managed Outbrain’s Anti-Fraud domain. She is currently the Head of Anti-Fraud at MoonActive.
Keren has a BSc in Industrial Engineering from Tel Aviv University and an MSc in Computer Science from the Interdisciplinary Center Herzliya.
When detecting fraud using AI, the challenge at hand has two different aspects – while one is about creating the model, which the unsupervised approach can assist, the other deals with the matter of how to evaluate the model. This is not a case where you want a model to be more efficient than a human, you need your model to flag fraud when you don’t even know to do it on your own. how can you evaluate and ultimately choose between different models?
All that and more will be covered during this session.
When detecting fraud using AI, the challenge at hand has two different aspects – while one is about creating the model, which the unsupervised approach can assist, the other deals with the matter of how to evaluate the model. This is not a case where you want a model to be more efficient than a human, you need your model to flag fraud when you don’t even know to do it on your own. how can you evaluate and ultimately choose between different models?
All that and more will be covered during this session.
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