Current churn prediction research mainly emphasizes static accuracy on curated datasets, and they often overlook label noise arising from heuristic CRM rules, uncontrollable user attrition, and suboptimal retention timing. This study proposes a robust two-stage retention framework targeting e-commerce users with tenure of more than four years. Firstly, structural churn cases are filtered from the dataset. Then, 5% label noise is added to simulate real-world annotation errors, and benchmark evaluations are conducted across nine machine learning models. Ensemble methods, specifically GBM, XGBoost, and LightGBM, achieve F1 scores ranging from 0.43 to 0.44 with cross-validation standard deviations below 0.02. About the method, the analysis progresses from binary classification to Cox proportional hazards modeling, identifying a proactive intervention window between the fourth and fifth year of user tenure. Business-oriented threshold tuning enables GBM campaigns to attain a 345.7% return on investment. This significantly surpasses simple methods. Further analysis via SHAP values shows service call fatigue is the primary churn driver. The assumed impact of fee sensitivity lacks empirical support within this mature cohort. The proposed project effectively bridges statistical prediction and operational retention, providing an effective methodology for protecting high-value customer assets.

The Black–Scholes option pricing model assumes that the volatility of the underlying asset is constant, yet observed market option prices imply a volatility that changes with the strike price. This study investigates whether adjusting the volatility input to match market prices improves option pricing and delta hedging, and whether the benefit differs between a high-volatility individual stock and a broad market index. Daily option data for Tesla and the SPDR S&P 500 exchange-traded fund from 2019 to 2022 are used, covering calm markets, the 2020 market crash, and the subsequent monetary tightening. Two settings are compared: one using historical volatility estimated from past returns, and one using an implied volatility obtained by fitting the Black–Scholes price to market option prices through least squares. Pricing accuracy is assessed by the pricing residuals across strike prices, and hedging effectiveness by the variability of the hedging profit and loss over forty-eight monthly positions. The results show that a single fitted volatility cannot match option prices at all strike prices, confirming that the constant-volatility assumption does not hold. Using the fitted volatility reduces the variability of the hedging profit and loss by about twelve percent for the individual stock and twenty-three percent for the index. The larger improvement for the index indicates that the value of calibration depends on the volatility structure of the underlying asset.

How Artificial Intelligence reshapes wealth distribution has become a key issue. Existing research is mainly divided into two independent branches: within-country and between-country studies. This paper aims to integrate these two dimensions by constructing a unified analytical framework. Firstly, the three factors affecting the distribution effect of Artificial Intelligence have been identified as the industrial structure, institutional environment and position in the Global Value Chain. Based on the empirical evidence from the United States, China and Latin America, the review shows that the inequality patterns within different countries and regions vary due to institutional and structural differences. At the international level, this paper links forecast data from the International Monetary Fund with potential causal paths to show how Artificial Intelligence is concentrating high-value-added activities in developed countries and driving developing countries into low-skilled and easily replaceable segments, thus widening the North-South divide. By connecting these two perspectives, this review provides a more comprehensive understanding of the uneven distribution of wealth caused by Artificial Intelligence.

Credit card fraud has become a central issue for the financial security of banks and cardholders. However, fraudulent transactions account for only a very small proportion of all transaction cases. If it is missed, it may directly cause huge property damage. As a result, this study focuses on how to identify fraudulent transactions as much as possible, while controlling false positives and human audit costs. Specifically, logistic regression, gradient boosting, XGBoost, and random forest are compared, while class weighting, SMOTE, and Random Undersampling are evaluated for handling class imbalance. It is more critical to translate the model results into a risk warning system and a loss simulation system. The results show that random forest with class weighting has the best overall performance, with 90.6% F1-Score, 94.1% precision, and 87.3% recall. Meanwhile, under the cost assumptions, this model reduces simulation costs by 85.4% relative to the no-model baseline. These findings suggest that in data environments with severe class imbalances, a machine learning model can be designed as an effective triage tool. Its actual value is not only to predict fraud, but also to establish a low-risk release, medium-risk verification, and high-risk manual review of the decision-making process for institutions and truly reduce losses.