Predicting real-time transaction fraud using supervised learning

Predicting transaction fraud of debit and credit card payments in real time is an important challenge, which state-of-art supervised machine learning models can help to solve. Sami Niemi offers an overview of the solutions Barclays has been developing and testing and details how well models perform in variety of situations like card present and card not present debit and credit card transactions.

Predicting transaction fraud of debit and credit card payments in real time is an important challenge, which state-of-the-art supervised machine learning models can help to solve. While supervised learning techniques, like logistic regression and neural networks, have been used for many years, recent developments in deep learning, gradient-boosted machines, and recurrent neural networks have opened up a wealth of options that can provide significant improvements over the existing models. These techniques are in general well suited for transaction fraud, but large data volumes (billions of transaction per year), very imbalanced target classes, ever-changing fraud MOs, and strict requirements for the prediction inference speed mean that some methods are better suited than others. Sami Niemi offers an overview of the solutions Barclays has been developing and testing and details how well models perform in variety of situations like card present and card not present debit and credit card transactions. Sami demonstrates how to train supervised transaction fraud models that can be implemented and how these models improve both customer experience and help to reduce fraud losses. He then explores results of a machine learning model that is operating in production.