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

ArXiv ID: 2406.19399 “View on arXiv”

Authors: Unknown

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.

Keywords: LSTM, state-space graph embeddings, customer goal prediction, reinforcement learning simulator, sequential decision making, General Financial Services

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: 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.

  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"]