Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach
<|reference_start|>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.<|reference_end|>
@article{estornell2024predicting,
    title={Predicting Customer Goals in Financial Institution Services: A
  Data-Driven LSTM Approach},
    author={Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel
  Borrajo, Rui Silva},
    journal={arXiv preprint arXiv:2406.19399},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19399},
    primaryClass={q-fin.ST cs.AI cs.CE}
}