The talk will cover some applications and use-cases of deep neural network architectures applied to the problem of payments fraud detection. With the multi-fold objectives such as maximizing fraud catch rate, approving the good user volume reliably and quickly, the underlying problem formulation and considerations applicable to large-scale online payment transaction data: such as dimensionality reduction, sparsity, high cardinality and temporality will be covered. Covering an assortment of deep learning methodologies applied to each problem formulation, some empirical comparisons and results will be presented. Lastly, some high level aspects of run-time performance benchmarking as applicable to training/inferencing processes and model deployment at PayPal will be presented.
Distinguished Data Scientist
05:00PM - Day 1