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

By incorporating state-space graph in recent years, driven by rapid technological advancements, embeddings into the LSTM model, we further enrich the evolving customer expectations, and increased model's understanding of the relationships and dependencies competition. As customers demand more personalized and among various features within the dataset, which may convenient services, financial institutions are under pressure lead to improved performance. This combination of LSTM to develop a deeper understanding of their clients' needs and models and state graph embeddings offers a more scalable preferences. This has led to a growing interest in leveraging and efficient solution in predicting customer goals and actions, data-driven approaches to gain insights into customer behavior while maintaining a high level of accuracy and robustness and predict future actions.