Data-driven marketing in the banking sector

: the role of machine learning and interactive interfaces in decision making

Abstract

As technological advancements reshape industries, the banking sector must also evolve. Despite the increasing availability of data, commercial banks often struggle to integrate it into their decision-making frameworks, mainly due to concerns about operational safety and profitability. A key research question is addressed during this study: How can commercial banks effectively integrate data analytics into their decision-making frameworks and further enhance the efficiency of data-driven strategies? To address this, a public dataset was analyzed, and a predictive model was developed. After an initial comparison between XGBoost and Logistic Regression algorithms, XGBoost was selected due to its superior performance. Despite having a 7.15% lower accuracy against the other tested XGBoost versions, the chosen model was still preferred due to its better positive class predictions. Positive class predictions are a crucial factor in marketing campaigns where identifying potential customers is more important than overall accuracy. Machine Learning (ML) models are often criticized due to their lack of interpretability and usability. As such, designing an interface is crucial to meet the needs of various stakeholders, including marketing professionals and call center agents. This research addressed these challenges by developing an adaptive interface capable of real-time data analysis, and the implementation of a real-time prediction’s simulator. The synergy between the predictive model and the interface, provides users with meaningful insights into the model’s reasoning, addressing the 'black box' concerns often associated with ML.

Date of Award	4 Jul 2025
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Rita Ribeiro (Supervisor)