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. 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: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.
By Muhammad Jawad Mufti, Omar Hammad, Haitham Saleh, Muqaddas Gull
The paper presents a graph‑based method for detecting money‑mule accounts by treating a bank’s relational schema as a heterogeneous graph and scoring each account using multi‑hop neighbourhoods, per‑column encoders, relative‑time encoding, and HeteroGraphSAGE message passing. On a five‑year RBIH retail‑banking dataset of 400 million transactions, the model achieves 83 % precision and a 0.5 % false‑alarm rate, recovering nine out of ten held‑out mules and outperforming tree‑based baselines in recall while maintaining precision deep into the alert queue. The approach can be deployed directly on a bank’s existing database without constructing a custom feature graph.
By Owais Ahmad, Anirudh Deodhar
arXiv:2606. 17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online.
By Degala Pushpa Sri, Mayank Atreya, Lakshmi. H, Navin Chhibber, Mukesh Soni
Money-mule networks move criminal funds through many small transfers, each of which looks ordinary on its own. What marks an account as a mule lies outside any single transfer: in the counterparties a...
arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.
By Mohammadamin Dashti Moghaddam, Nick Sciarrilli
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:2606. 00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs.
By Andrei Bysik, Robert \'Slepaczuk
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:2608. 15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions.
By Emmanuel Nahimana, Ya\'e Ulrich Gaba
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