A practical guide to combining pre-churn scoring with uplift modelling for smarter retention. The post How to Improve Customer Retention in FinTech appeared first on Towards Data Science .
The article discusses how model validation standards are evolving for large language model (LLM) based systems, particularly in the banking sector. It examines what aspects of traditional validation break down, what elements remain applicable, and how to effectively test the quality of LLM outputs. The piece offers practical insights into adapting validation playbooks for generative AI applications.
arXiv:2607. 10260v1 Announce Type: new Abstract: Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships.
How unit economics should set your classification cutoff, and why they rarely do. The post Your Churn Threshold Is a Pricing Decision appeared first on Towards Data Science .
arXiv:2608.30364v1 Announce Type: new Abstract: Retail banking attrition is usually represented as a terminal binary event, even though client relationships often weaken earlier through partial movem...
The paper audits the IBM Telco Customer Churn benchmark, revealing that common practices inflate performance metrics. It shows that pre‑split SMOTE boosts churn‑class F1 by 13.1 points, that isotonic regression is the best calibration method while temperature scaling fails on tree ensembles, and that the cost‑optimal decision threshold is 5–10 times lower than the F1‑optimal one, saving about $77,000 per 1,000 customers. The authors also test generalisation on Iranian Telecom and Bank churn datasets, and propose a four‑component reporting checklist with reproducible code.
arXiv:2608.20447v1 Announce Type: new Abstract: Feature selection is a highly relevant task in a data-driven knowledge discovery project. Several techniques have been developed aiming at finding the...
The article recounts a final‑year project in which the author trained six different models for fraud detection. It highlights the discrepancy between the model that performed best on evaluation metrics and the one that was ultimately chosen for production. The piece reflects on how real‑world constraints can override purely statistical performance.
The paper presents a framework for integrating explainable AI into customer churn prediction for telecommunications. It benchmarks four classifiers—Logistic Regression, Random Forest, XGBoost, and LightGBM—on the IBM Telco Customer Churn dataset, finding comparable performance with Logistic Regression achieving the highest AUC-ROC and LightGBM the highest accuracy. Explanations are provided via SHAP and LIME at both global and instance levels, and a four‑layer CRM integration architecture is proposed to translate risk scores and attribution vectors into actionable retention strategies, projecting a 3.3–5.3 percentage point reduction in churn and $199K–$319K savings per campaign cycle.
The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .
Nubank elevates customer experiences with OpenAI
arXiv:2606. 06776v1 Announce Type: new Abstract: Customer churn prediction is a central task in customer analytics, particularly in non-contractual, pay-per-use service environments where disengagement is not explicitly observed and must be inferred from behavioral inactivity.