Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach

Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel Borrajo and Rui Silva

Papers from arXiv.org

Abstract: In today's competitive financial landscape, understanding and anticipating customer goals is crucial for institutions to deliver a personalized and optimized user experience. This has given rise to the problem of accurately predicting customer goals and actions. Focusing on that problem, we use historical customer traces generated by a realistic simulator and present two simple models for predicting customer goals and future actions -- an LSTM model and an LSTM model enhanced with state-space graph embeddings. Our results demonstrate the effectiveness of these models when it comes to predicting customer goals and actions.

Date: 2024-05
New Economics Papers: this item is included in nep-inv
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