Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge
This work addresses information management challenges for banks by providing a more efficient solution for customer-related tasks, though it appears incremental as it builds on existing methods.
The paper tackled the problem of costly separate models for local and global customer tasks in banks by comparing unsupervised methods and introducing a novel approach that incorporates external client information, achieving up to 20% accuracy improvement.
In today's world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categorized into two groups: 1) local ones, which focus on a client's current state, such as transaction forecasting, and 2) global ones, which consider the general customer behaviour, e.g., predicting successful loan repayment. Unfortunately, maintaining separate models for each task is costly. Therefore, to better facilitate information management, we compared eight state-of-the-art unsupervised methods on 11 tasks in search for a one-size-fits-all solution. Contrastive self-supervised learning methods were demonstrated to excel at global problems, while generative techniques were superior at local tasks. We also introduced a novel approach, which enriches the client's representation by incorporating external information gathered from other clients. Our method outperforms classical models, boosting accuracy by up to 20\%.