Advances in technology and an abundance of data have made machine learning a key component in the fight against evolving fraud. Find the best solution suited for a business' specific needs requires investigating the types of machine learning models in use, the datasets that trained them, the combination of data being leveraged by the models and their approach to obtaining truth data.
In this session, our experts will explain the key concepts and approaches to machine learning, including:
- The difference between Supervised vs unsupervised machine learning techniques and when to apply them
- Applications and challenges of machine learning in the fraud domain
- How automatic rule recommendations can help businesses stay ahead of evolving fraud
- How AutoML can be used to improve efficiency in supervised learning
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