Businesses are developing new methods to identify fraud with in-memory machine learning models for predictive analytics. By moving these models to in-memory technologies, data scientists are able to increase both the speed and accuracy of model development and performance.
In this paper, we will introduce machine learning and explain its role in fraud detection. We will cover the challenges that data science teams face in deploying their models. Then we will explore the business implications of succumbing to the status quo, along with the technology implications to deliver in-memory machine learning for fraud detection in the real world.