Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866911935461588992 |
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| author | Estornell, Andrew Vasileiou, Stylianos Loukas Yeoh, William Borrajo, Daniel Silva, Rui |
| author_facet | Estornell, Andrew Vasileiou, Stylianos Loukas Yeoh, William Borrajo, Daniel Silva, Rui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach Estornell, Andrew Vasileiou, Stylianos Loukas Yeoh, William Borrajo, Daniel Silva, Rui Statistical Finance Artificial Intelligence Computational Engineering, Finance, and Science 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. |
| title | Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach |
| topic | Statistical Finance Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2406.19399 |