BRec the Bank: Context-aware Self-attentive Encoder for Banking Products Recommendation

Credit cards, deposits, loans, pension funds, mutual funds-which of these products are relevant to a bank's clients, and at what time in their banking journey? We propose a modeling framework for item recommendation using a multi-head self-attentive encoder and a novel sequential input data representation accounting for the temporal context of both item ownership and user metadata. We evaluate our model on a large public dataset from Bank Santander, and achieve top-1 and top-5 precision of 98.9% and 40.2%, respectively, thereby improving upon a number of state-of-the-art recommendation models also based on self-attention. We find evidence that our model is effective at learning, from the temporal context of product acquisitions, user behavior that is more informative to recommendations than a bag of products or static user metadata. Further, we consider serendipity, novelty and coverage to exhibit a trade-off with recommendation relevance. We also analyze ethical considerations related to the banking products recommendation task. The continuous user representation learned by our model may inform decisions far more impactful than user-level product recommendations themselves.