Paper: arXiv 2406.19399

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.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses a straightforward LSTM model with handcrafted features on a simulated dataset, showing low mathematical complexity; however, it employs a realistic simulator and presents specific accuracy metrics for goal prediction, demonstrating solid empirical implementation.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict Customer Goals in Financial Services"] --> B["Methodology<br>LSTM & LSTM with Graph Embeddings"]
  B --> C["Data Source<br>Historical Traces from Simulator"]
  C --> D["Computational Process<br>Sequential Modeling & Prediction"]
  D --> E["Key Outcome<br>Accurate Prediction of Goals & Actions"]