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.
By Sandeep Gaddamwar
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.
By Syed Saad Saif, Giulio Maggiore, Paolo Russo, Damiano Distante
arXiv:2607. 10260v1 Announce Type: new Abstract: Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships.
By Nada Ali, Lina Ahmed, Tahani Abdalla Attia Gasmalla
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.
By Joyjit Roy, Samaresh Kumar Singh, Laxmi Shaw
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...
By MinJoo Kim, SanJin Park, SeungHwan Cho
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...
By Ananyaa Chopra, Brandon Xu, Brendan Yuen, Lauren Zung, Sarabroop Aulakh
The paper introduces DTD‑VAE, a Variational Autoencoder that disentangles temporal dependencies to better predict credit risk. It uses an autoregressive feature inference module to capture temporal patterns among latent variables and an element‑wise gating mechanism in the generative module to assign independent weights to each latent dimension, especially those relevant to credit risk. Experiments on six real‑world datasets show the model outperforms existing methods, improving ROC‑AUC by 3.2%–4.86% and Accuracy Ratio by 6.41%–9.71%.
By Xiaobo Guo, Lu-an Dong, Yanbo Wang, Peng Zhang, Cai Zhi, Youru Li
arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.
By Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn
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.
By Soumyadeep Roy
The paper proposes a shift from predictive modeling to descriptive provider behavior profiles for fraud, waste, and abuse (FWA) review. By decomposing billed revenue into provider scale and procedure composition, the authors construct lineage‑aware profiles that capture scale history, code lineage, and clinical family shares. In a large Medicare audit, these simple, interpretable descriptions outperform complex forecasts and improve recall for high‑cost rare events, while an optional semantic factorization adds context without inferring intent.
By Yubin Park, Evan Brociner
arXiv:2607. 00473v1 Announce Type: new Abstract: How many days of early behavior suffice for subscription churn prediction?
By Xiao Han, Yao Xiao, Chenyu Wu, Tongchen Zhang
The paper proposes replacing multiple horizon‑specific binary classifiers with a single survival model to predict time‑to‑repurchase in grocery e‑commerce. Empirical analysis shows a slightly decreasing hazard (k≈0.9) and that a Log‑Normal model best fits marginal distributions while Weibull best fits residuals. A single Accelerated Failure Time (AFT) model matches or surpasses per‑horizon classifiers with fewer trees, and a 4‑parameter calibration maps survival CDFs to horizon probabilities without monotonicity violations, revealing a trade‑off between calibration and ranking within the AFT family.
By Akshay Kekuda, Shreeranjani Srirangamsridharan, Ishan Bhatt, Yanan Cao, Sinduja Subramaniam, Evren Korpeoglu, Kaushiki Nag, Kannan Achan