Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach
This work addresses the need for personalized user experiences in financial institutions, but it appears incremental as it applies existing LSTM methods to a specific domain.
The paper tackled the problem of predicting customer goals and actions in financial services using historical customer traces from a simulator, presenting an LSTM model and an enhanced version with state-space graph embeddings, and demonstrated their effectiveness in prediction.
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.