Paper: arXiv 2507.01987

Authors: João B. G. de Brito, Rodrigo Heldt, Cleo S. Silveira, Matthias Bogaert, Guilherme B. Bucco, Fernando B. Luce, João L. Becker, Filipe J. Zabala, Michel J. Anzanello

Abstract

The emergence of Open Banking represents a significant shift in financial data management, influencing financial institutions’ market dynamics and marketing strategies. This increased competition creates opportunities and challenges, as institutions manage data inflow to improve products and services while mitigating data outflow that could aid competitors. This study introduces a framework to predict customers’ propensity to share data via Open Banking and interprets this behavior through Explanatory Model Analysis (EMA). Using data from a large Brazilian financial institution with approximately 3.2 million customers, a hybrid data balancing strategy incorporating ADASYN and NEARMISS techniques was employed to address the infrequency of data sharing and enhance the training of XGBoost models. These models accurately predicted customer data sharing, achieving 91.39% accuracy for inflow and 91.53% for outflow. The EMA phase combined the Shapley Additive Explanations (SHAP) method with the Classification and Regression Tree (CART) technique, revealing the most influential features on customer decisions. Key features included the number of transactions and purchases in mobile channels, interactions within these channels, and credit-related features, particularly credit card usage across the national banking system. These results highlight the critical role of mobile engagement and credit in driving customer data-sharing behaviors, providing financial institutions with strategic insights to enhance competitiveness and innovation in the Open Banking environment.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies established machine learning algorithms (XGBoost, ADASYN, SHAP) to a massive real-world dataset of 3.2 million banking customers to address a novel business problem: predicting Open Banking data sharing. While mathematical derivations are minimal, the empirical design, handling of severe class imbalance, and interpretability pipeline are highly rigorous and practically valuable.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict & Explain Customer Data Sharing in Open Banking] --> B[(Data Input: ~3.2M Customers from Brazilian Bank)]
    B --> C[Data Balancing: Hybrid ADASYN + NEARMISS]
    C --> D[Predictive Modeling: XGBoost]
    D --> E[Explanatory Model Analysis: SHAP + CART]
    E --> F[High Accuracy Outcomes: 91.39% Inflow / 91.53% Outflow]
    F --> G[Key Insights: Mobile Channel Engagement & Credit Card Usage Drive Decisions]