Boost Fraud Detection with More Data, More Insight
- 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.