Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach
Statistical Finance
2024-07-01 v1 Artificial Intelligence
Computational Engineering, Finance, and Science
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.
Keywords
Cite
@article{arxiv.2406.19399,
title = {Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach},
author = {Andrew Estornell and Stylianos Loukas Vasileiou and William Yeoh and Daniel Borrajo and Rui Silva},
journal= {arXiv preprint arXiv:2406.19399},
year = {2024}
}
Comments
Accepted at the FinPlan 2023 workshop at ICAPS 2023