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arXiv:2607. 10260v1 Announce Type: new Abstract: Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships.
arXiv:2608. 07471v1 Announce Type: cross Abstract: This study considers the task of applying artificial intelligence to recognize bank fraud.
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...
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. 14209v1 Announce Type: new Abstract: Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple base learner.
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...