Build an explainable next-best-product recommendation system for banking on AWS

The article presents a deep learning-based explainable next-best-product recommendation system for banking institutions using Amazon SageMaker AI and PyTorch. The system utilizes a multi-tower neural network architecture with four specialized towers, each processing a different aspect of customer data, and provides per-customer explainability through learned attention. The solution is designed to work with AWS services, including SageMaker AI, Amazon S3, AWS Glue, and Amazon CloudWatch. The practical implication for engineers building AI systems is the ability to design more accurate and interpretable models for recommendation systems in financial services and other domains with heterogeneous customer data.

⚡ Key Takeaways

• The solution uses a multi-tower deep learning architecture with four specialized neural network towers.
• The system provides per-customer explainability through learned attention mechanisms.
• The required packages include Python 3.11+, PyTorch 2.9+, Pandas 2.3+, NumPy 2.3+, scikit-learn 1.7+, and Dask 2025.11+.
• The solution requires an AWS Identity and Access Management (IAM) execution role with access to SageMaker AI, Amazon S3, AWS Glue, and Amazon CloudWatch.
• The recommended AWS services include SageMaker AI, Amazon S3, AWS Glue, and Amazon CloudWatch.

The article provides a concrete example of how to build an explainable next-best-product recommendation system for banking institutions using Amazon SageMaker AI and PyTorch, which can help engineers design more accurate and interpretable models for recommendation systems in financial services and other domains. The solution's use of a multi-tower neural network architecture and learned attention

✅ Practical Steps

1. Create an AWS account with permissions for SageMaker AI, Amazon S3, AWS Glue, and Amazon CloudWatch.
2. Set up an IAM execution role with access to the required AWS services.
3. Install the required packages, including Python 3.11+, PyTorch 2.9+, Pandas 2.3+, NumPy 2.3+, scikit-learn 1.7+, and Dask 2025.11+.
4. Design a multi-tower neural network architecture with four specialized towers to process different aspects of customer data.

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