Boost Fraud Detection with More Data, More Insight

Apr 12 2021 | PayPal Editorial Staff

Machine learning-powered fraud detection and data go hand in hand. Learn more about the impact that size and volume of data have in creating powerful fraud models.
Key Takeaways
  • As fraud continues to evolve, different types of data are required to build effective machine learning models for different fraud use cases.
  • The question of not only what data, but how much data is required to build these models is one of the most common questions asked during the process — but there are important factors to consider, which affect the quantity of data needed for a model.
  • Machine learning models are only as good as the data fed into them. Think not just in terms of quantity, but of quality, as well.
The content of this article is provided for informational purposes only. You should always obtain independent business, tax, financial, and legal advice before making any business decisions.

Was this content helpful?

We’ll use cookies to improve and customize your experience if you continue to browse. Is it OK if we also use cookies to show you personalized ads? Learn more and manage your cookies