AWS multi-tower neural net powers explainable next-best-product banking recommendations

Banking institutions hold vast amounts of customer data—transaction histories, product ownership, demographics, and behavioral patterns—but struggle to translate this into actionable, personalized product recommendations. Traditional rule-based or collaborative filtering approaches fail to capture complex temporal patterns in customer product adoption journeys. AWS presents a Next-Best-Product (NBP) recommendation system built on a multi-tower deep learning architecture with four specialized neural network towers. Each tower processes a different data type: transactions, product ownership, demographics, and behavior. These towers are fused using a learned attention mechanism that delivers high accuracy and per-customer explainability, critical for banking regulatory requirements. The system is trained on Amazon SageMaker AI using GPU instances (e.g., ml.g5.12xlarge) and uses AWS Glue for serverless ETL at scale.

The tech stack leverages PyTorch for its dynamic computation graphs and research-to-production flexibility, alongside Pandas, Dask, and NumPy for feature engineering. The attention mechanism enables the model to show which factors influenced each recommendation, meeting transparency mandates. While the post provides architectural guidance (not a step-by-step deploy), it lists prerequisites including an AWS account with SageMaker, S3, Glue, and CloudWatch permissions, and familiarity with deep learning. This pattern extends beyond banking to any domain with heterogeneous customer data, offering a scalable and interpretable recommendation solution.