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:2607. 17586v1 Announce Type: cross Abstract: Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data.
By Yuge Zhang, Yuanxing Zhang, Yichao Jin, Khairul Amsyar Mohd Razis, Nicholas Qi An Choo, Kai Yin Anders Wong, Xinyan Tang, Kenneth Zhu Ke, Wee Keong Dennis Lee, Jingyuan Zhao
The paper investigates how personalized agents decide to use, ignore, update, or query retrieved user memory before acting on a task. An empirical audit protocol is developed to test structured intermediate outputs, revealing that while exposing state definitions improves accuracy, an explicit state-output field does not significantly enhance policy accuracy for large language models. The study also shows that example-level accuracy overstates consistency, with full four‑way family success being rare, and that providing benchmark‑associated state labels merely conditions predictions rather than proving internal fidelity.
By Yihang Chen, Pin Qian, Su Wang, Chong Peng, Huan Xu, Shuaiting Li, Yiqi Sun
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
By Rahil Sharma
MemGuard-Alpha evaluates whether membership inference attacks (MIA) can detect memorization in large language models (LLMs) used for financial alpha signals. The study combines five MIA methods with a temporal proximity feature and a cross-model disagreement metric, then audits them across seven LLMs, 50 S&P 100 stocks, and 299,600 prompt-model pairs. Findings show that temporal proximity alone perfectly predicts in-sample status, MIA discriminative power largely stems from model scale differences, and filtering based on contamination scores does not improve risk-adjusted performance once transaction costs are considered.
By Anisha Roy, Dip Roy
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