Build an explainable next-best-product recommendation system for banking on AWS
AWS Machine Learning Ayush Singh Chauhan
AWS published an architectural guide for building a deep learning-based product recommendation system for banks using SageMaker and PyTorch, with four specialized neural network towers processing different customer data types. The system uses a multi-tower architecture with GRU for sequence processing and attention mechanisms for learned fusion, requiring ml.g5.12xlarge GPU instances (192 GB RAM, 4× NVIDIA A10G GPUs) for training. Banks can now provide personalized product recommendations with built-in explainability that addresses regulatory requirements without needing post-hoc interpretation methods.
Why it matters
Learn the architecture and design decisions behind an explainable next-best-product recommendation system for banking, built with Amazon SageMaker AI and PyTorch. A multi-tower neural network with learned attention delivers accurate, per-customer recommendations while providing the explainability that banking regulators require.
