Predicting and Explaining Customer Data Sharing in the Open Banking

TL;DR

This paper presents a predictive framework using machine learning to determine customer data sharing behavior in Open Banking, along with interpretability analysis revealing key influencing factors.

Contribution

It introduces a hybrid data balancing approach and combines XGBoost with EMA for accurate prediction and explanation of customer data sharing in Open Banking.

Findings

Achieved over 91% accuracy in predicting data inflow and outflow.

Identified mobile transactions and credit usage as key factors influencing sharing behavior.

Demonstrated the effectiveness of combining SHAP and CART for interpretability.

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.