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.
arXiv:2607. 10260v1 Announce Type: new Abstract: Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships.
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:2606. 07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where retaining existing customers is typically more cost-effective than acquiring new ones.
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.
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...
arXiv:2609.09766v1 Announce Type: new Abstract: Churn models typically identify high-risk customers but do not specify which feasible retention action should be considered or why that action is appro...
arXiv:2606. 00169v1 Announce Type: cross Abstract: Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors.
arXiv:2608.20343v1 Announce Type: new Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ense...
arXiv:2609.37223v1 Announce Type: new Abstract: Credit-risk prediction is important in banking, but a prediction alone does not explain why an applicant is risky or how it should be combined with oth...
arXiv:2608.30086v1 Announce Type: cross Abstract: Credit-default prediction is an important task in financial decision making. Traditional methods use fitted classifiers such as logistic regression a...
arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.
Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false negatives leave harmful activity undetected. The hardest cases require jointly understanding a merchant's textual profile and long behavioral sequence.