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
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
arXiv:2607. 27859v1 Announce Type: cross Abstract: Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations.
By Girish G N, Ashutosh Sahoo, Ajay Bhat, Akshay SP, Gurukiran S, Parag Paul, Dhanashekar Kandaswamy
arXiv:2608. 08126v1 Announce Type: new Abstract: Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters, in tension with supervisory expectations that adverse decisions be explainable.
By Gregorius Reynaldi Pratama, Kuo-Kun Tseng
arXiv:2609.09945v1 Announce Type: cross
Abstract: Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where ap...
By Gijs A. F. Niewzwaag, Marijn G. S. Veth, Manuele Massei, Marcos R. Machado
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
Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness.
arXiv:2609.25542v1 Announce Type: new
Abstract: Corporate default prediction is a core problem in financial risk management, yet traditional credit models rely heavily on financial statements that ar...
By Junghoon Kim, Hyunsung Kim, Seungyoon Choi, KyoungYong Park, Jihun Lee, YongGu Ji, Chanyoung Park
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
The paper examines how a breach of a single AI vendor—used by banks for fraud screening, credit decisions, AML triage, customer analytics, and internal support—can spread through operational, informational, and financial links, ultimately causing losses that resemble a traditional banking crisis. It introduces a four‑layer heterogeneous network linking AI vendors, banks, interbank exposures, and customer accounts, and presents CFC‑Prop, a stochastic epidemic‑and‑clearing model that reproduces heavy‑tailed loss distributions and sensitivity to patch latency on a synthetic dataset of 60 vendors, 220 banks, and 1,400 interbank exposures. Additionally, the authors develop CFC‑GNN, an early‑warning graph‑based model that predicts high‑cascade‑risk vendors with AUROC 0.82 and AUPRC 0.60, and they release all code, data, and scripts for reproducibility.
By Alex Leytes
arXiv:2605. 23955v3 Announce Type: replace Abstract: Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critical vulnerabilities in algorithmic reproducibility.
By Ruizhe Zhou, Xiaoyang Liu, Gaoyuan Du, Yi Zheng, Shouxi Ren, Deepayan Chakrabarti, Dengdu Jiang
arXiv:2608.22389v1 Announce Type: cross
Abstract: Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This remo...
By Ahmed Abolfadl