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

TL;DR

This paper introduces LSTM-based models, including an enhanced version with graph embeddings, to predict customer goals and actions in financial services, aiming to improve personalized user experiences.

Contribution

It presents a novel application of LSTM models with graph embeddings for predicting customer behavior using simulated data in finance.

Findings

LSTM models effectively predict customer goals and actions.

Enhanced LSTM with graph embeddings improves prediction accuracy.

Models outperform baseline approaches in simulated financial scenarios.

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.