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

arXiv: https://arxiv.org/abs/2406.19399 · HTML: https://mlsanity.com/paper/2406.19399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Active Goal Recognition](https://mlsanity.com/paper/1909.11173/) (2019)
- [Goal recognition via model-based and model-free techniques](https://mlsanity.com/paper/2011.01832/) (2020)
- [Domain-independent generation and classification of behavior traces](https://mlsanity.com/paper/2011.02918/) (2020)

## Abstract (arXiv)

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.