Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel Borrajo, Rui Silva
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Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach
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.