AWS blog: next-best-product recommendation system for banking using deep learning

Banks have long struggled to convert their rich internal datasets into actionable product recommendations. The article identifies a specific gap: traditional rule-based systems and collaborative filtering approaches cannot capture the temporal order in which customers adopt products—they see only a static list of what each customer owns, not the journey of how they acquired those products. The multi-tower architecture addresses this by assigning different neural network towers to different data modalities (sequences, transactions, demographics, behavior), each optimized for its data type, rather than forcing all data through a single generic pathway. This specialization both improves model capacity utilization and creates a clearer path to explainability: the learned attention mechanism that fuses the towers can show, per customer, which towers (and therefore which aspects of their profile) most influenced the recommendation.

The technology choices reflect production considerations for financial services. AWS Glue provides serverless ETL for normalizing data arriving from multiple banking systems with inconsistent schemas—a universal challenge in banking. The use of Parquet with Snappy compression on S3 offers both cost efficiency (3–5× compression over CSV) and performance gains (column pruning, predicate pushdown). PyTorch was selected for its dynamic computation graphs, which the article notes are necessary to handle variable-length customer sequences via pack_padded_sequence, and for its native SageMaker AI integration. The data pipeline is split into two stages: AWS Glue for schema unification and temporal feature engineering, then Amazon SageMaker Processing for ML-specific operations like sequence padding and windowed aggregations across 7-, 30-, 60-, 180-, and 365-day windows.