Multi-Performance Estimation for Deploying Bank Branches Based on a Multi-Task Attentive Tree-Enhanced Model

Abstract

Selecting the optimal location for a new bank branch is challenging but worth studying due to its importance to growing a successful business. Bankers have been devoting a lot of effort to this issue. In recent years, the proliferation of multisource data in smart cities has called for more need for data-driven optimal location selection. Previous studies usually focus on mining different features and processing these data through domain expert knowledge. However, they fail to discover potential complicated factors interactions. Besides, most of these studies only use a single indicator to evaluate the candidate locations, which overlooks a lot of information, resulting in an inaccurate evaluation. In this study, we propose MATE, a Multi-task Attentive Tree-Enhanced model, to simultaneously predict multiple performance indicators of a bank branch's location. Our model takes advantage of the tree-based model, which can effectively extract cross features and provide interpretability according to inferred decision rules. In addition, we designed the financial embedding module and attentive interaction blocks, allowing MATE to obtain more complex and diverse features. Finally, extensive experiments on real-world bank datasets demonstrate the effectiveness of our method, which outperforms baseline and state-of-art methods from 17% to 41%.