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Podcast / Machine Learning
Ravelin’s senior data scientist explains the basics of machine learning and dives deep into what features, scores, and models are.
Ravelin requires significant amounts of data from clients in order to provide the predictions as to which transactions are likely to result in chargebacks. In return, it is our commitment to our clients to use that data to the utmost degree, ensuring that we extract the most information possible to ensure we get the most predictions right. It’s not perfect; perfection is an illusion in fraud detection. It is however optimal based on the premise that any online business wants to accept as many good transactions as possible.
In this podcast, Stephen Whitworth, Senior Data Engineer at Ravelin explains the basics of machine learning and discusses features, scores, and models. To learn more about why machine learning is the perfect tool for fraud prevention read more about this here.
Alara Basul Head Of Content
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Blog / Fraud Analytics
Fraud prevention is a delicate balance between stopping fraud and maintaining good customer experiences. But what is the most effective way to measure this outcome?
Ravelin Technology, Writer
Blog / Machine Learning
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