Sequential Banking Products Recommendation and User Profiling in One Go

Abstract

How can banks recommend relevant banking products such as debit, credit cards or term deposits, as well as learn a rich user representation for segmentation and user profiling, all via a single model? We present a sequence-to-item recommendation framework that uses a novel input data representation, accounting for the sequential and temporal context of both item ownership and user metadata, fed to a multi-head self-attentive encoder. We assess the performance of our model on the largest publicly available banking product recommendation dataset. Our model achieves 98.9% Precision@1 and 40.2% Precision@5, outperforming a state-of-the-art model as well as a common XGBoost-based baseline model tailored for this dataset and a system reportedly employed in industry for this task. Next, using the encoder embedding we obtain a continuous representation of users and their past product behavior. We demonstrate, in a case study, that this representation can be used for user segmentation and profiling, both critical to decision-making in organizations; for example, in designing and differentiating value propositions. The proposed approach is more inclusive and objective than the traditional ones employed by banks. With this work, we expose the benefits of employing a recommendation model based on self-attention in a real-world setting. The continuous user representation learned can yield far more impact than individual user-level recommendations. Both the proposed model and approach to segmentation and profiling are also applicable in other industries, beyond banking.