Build an explainable next-best-product recommendation system for banking on AWS | Amazon Web Services
Building a deep learning-based explainable next-best-product recommendation system helps banking institutions predict which product a customer needs next. Banks hold vast amounts of customer data, including transaction histories, product ownership records, demographic profiles, and behavioral patterns. Translating this data into actionable, personalized product recommendations remains a significant challenge. Traditional rule-based systems and collaborative filtering approaches often fail to capture the complex temporal patterns in customer product adoption journeys.
In this post, we present the architecture and design decisions behind a Next-Best-Product (NBP) recommendation system using Amazon SageMaker AIand PyTorch. We explain the reasoning behind a multi-tower neural network architecture, how learned attention provides per-customer explainability, and how AWS services work together to take this solution from research to production. This is an architectural overview, not a step-by-step deployment guide. Whether you are building recommendation systems for financial services or other domains with heterogeneous customer data, the architectural patterns described...
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