Transformer-based Banking Products Recommender System
Davide Liu, George Philippe Farajalla, Alexandre Boulenger · 2021
Credit cards, deposits, loans, pension funds, mutual funds which of these products is relevant to a bank's clients, and at what time in their banking journey? We propose a modeling framework for item recommendation using a Transformer encoder [6] and a novel input data representation accounting for the temporal context of item ownership and user metadata. We evaluate the model on a large dataset from Bank Santander. Our system outperforms industry baselines Amazon Personalize [1], and XGBoost [4], a top performing model in the Santander Kaggle competition [2]. We achieve a 56.6% top-3 precision and significantly outperforms Amazon Personalize and the XGBoost model, with 21.5% and 37.9% top-3 precision, respectively. We engineered an original way of representing input data as a sequence and found that this specific representation, with our Transformer-based architecture, improves the model's performance. We hope that our contribution paves the way for the democratization of recommender systems in banking, and the use of the Transformer model for product recommendation in industry.