Evaluating multi-label classifiers and recommender systems in the financial service sector

This is the authors' abstract. We don't add key points for this paper.

The objective of this paper is to evaluate multi-label classification techniques and recommender systems for cross-sell purposes in the financial services sector. We carried out three analyses using data obtained from an international financial services provider. First, we tested four multi-label classification techniques, of which the two problem transformation methods were combined with several base classifiers. Second, we benchmarked the performance of five state-of-the-art recommender approaches. Third, we compared the best performing multi-label classification and recommender approaches with each other. The results identify user-based collaborative filtering as the top performing recommender system, with a cross-validated F1 measure of 42.20% and G-mean of 42.64%. Classifier chains binary relevance with adaboost and binary relevance with random forest are the top performing multi-label classification algorithms for respectively F1 measure and G-mean, yielding a cross-validated F1 measure of 53.33% and G-mean of 54.37%. The statistical comparison between the best performing approaches confirms the superiority of multi-label classification techniques. Our study provides important recommendations for financial services providers, who are interested in the most effective methods to determine cross-sell opportunities. In previous studies, multi-label classification techniques and recommender systems were always investigated independently of each other. To the best of our knowledge, our study is therefore the first to compare both techniques in the financial services sector.