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

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Listed:
  • Andrew Estornell
  • Stylianos Loukas Vasileiou
  • William Yeoh
  • Daniel Borrajo
  • Rui Silva

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.

Suggested Citation

  • Andrew Estornell & Stylianos Loukas Vasileiou & William Yeoh & Daniel Borrajo & Rui Silva, 2024. "Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach," Papers 2406.19399, arXiv.org.
    • Handle: RePEc:arx:papers:2406.19399
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    File URL: http://arxiv.org/pdf/2406.19399
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