Unique problems deserve unique solutions...
One size never fits all when it comes to fraud! Data Scientist Nathan Cheng shows us why custom models are the way forward for your business.
Your business has its own set of fraud challenges, so adopting a one size fits all approach to prevention just isn’t sufficient. Your fraud solutions should detect the most relevant signals to your business.
Learn answers to these questions:
- Why a custom model is the way forward for your business
- How to identify the traits specific to your fraudy customers
- How custom models keep learning over time as your fraud evolves
- How to balance your block rate and fraud rate to maximize revenue and minimize risk
Nathan Cheng is a Senior Data Scientist at Ravelin. He leads a team that builds custom machine learning models to detect and prevent fraud. Before joining Ravelin, Nathan worked on hydrogen fuel cells and studied a Masters in Theoretical Condensed Matter Physics.
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