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How machine learning can improve customer loss prevention: A case study in the banking sector
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.42 MB | Adobe PDF |
Orientador(es)
Resumo(s)
The banking industry faces mounting pressure to retain profitable clients, as acquiring new
ones is far costlier than preserving existing relationships. This study investigates how machinelearning–driven churn prediction can bolster customer-loss prevention for a Portuguese retail
bank. A proprietary dataset containing 10 000 customers and 141 behavioural, transactional
and demographic variables was explored under the CRISP-DM framework. After rigorous data
cleaning, feature engineering, class-imbalance handling (SMOTE) and multi-stage feature selection, seven supervised models were benchmarked: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Support Vector Machine and Artificial Neural Network. Initial experiments showed ensemble techniques consistently outperforming single learners across Accuracy, Precision, Recall, ROC AUC and F1-score.
Subsequent grid-search hyper-parameter tuning further improved results, with the tuned
XGBoost model emerging as the top performer (F1 = 0.702; Accuracy = 0.800). Key predictive
drivers included client tenure, recent debit-card activity, average deposit balances and digitalchannel engagement. These insights enable the bank to identify high-risk customers early and
deploy tailored retention levers, such as repricing, loyalty incentives or personalised product
bundles, before attrition occurs. Overall, the study demonstrates that a well-governed machine-learning pipeline can transform raw banking data into actionable intelligence, delivering
a scalable decision-support tool that underpins proactive, data-driven churn-mitigation strategies.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Banking Sector Customer Churn Machine Learning Predictive Models SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure