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The paper explores credit card fraud detection (CCFD) using machine learning, reviewing various algorithms like K-nearest neighbors, decision trees, random forests, and XGBoost. It compares their performance, highlighting Random Forest as the most accurate. The study addresses challenges like imbalanced datasets, data quality, and evolving fraud tactics.
This paper emphasizes the need for metrics to assess discriminatory effects and trade-offs. It introduces a sensitivity-based measure for proxy discrimination, defining admissible prices and using L2-distance for measurement, and proposes local measures for policyholder-specific analysis.
The paper examines optimal insurance solutions using $\Lambda\VaR$. It finds truncated stop-loss indemnity optimal with the expected value premium principle and provides a deductible parameter expression. Using $\Lambda'\VaR$, full or no insurance is optimal. It also addresses model uncertainty, offering solutions for various uncertainty scenarios.
This paper highlights the risks of assuming finite mean or variance in statistical models, especially for datasets with heavy tails, like in finance. It stresses that infinite-mean models can lead to different or opposite outcomes, requiring caution when applying classic methods in finance and insurance.
The paper develops a machine learning algorithm using financial data to identify Italian private firms linked to organized crime. By analyzing firms with Mafia connections, it achieves a 74.9% AUC and 91.4% precision. This method serves as a risk management tool and supports legal enforcement actions.
“ESG integration enhances bank stability and competitiveness, contributing to sustainable economic development.”
The Anti-Money Laundering (AML) regime has harmonized laws globally but lacks credible data on its effectiveness. Evaluations are inconsistent and infrequent, relying on outdated data. Without systematic analysis, claims of effectiveness may be considered subjective, undermining legitimacy despite potential impacts of AML efforts.
“We consider an insurance company which faces financial risk in the form of insurance claims and market-dependent surplus fluctuations. The company aims to simultaneously control its terminal wealth (e.g. at the end of an accounting period) and the ruin probability in a finite time interval by purchasing reinsurance… We solve the problem of finding the optimal reinsurance strategy and the corresponding maximal target functional via neural networks.”
Advancements in AI have reshaped risk analysis, emphasizing scalability, explainability, and simplified reporting. This paper urges the risk field to lead in establishing ethical standards and best practices, calling for research to develop guidelines for emerging applications in risk science."
The paper examines non-linearities in how geopolitical risk (GPR) shocks affect the economy. Using a VARX model, it finds that large GPR shocks (above 4 standard deviations) significantly increase uncertainty, leading to precautionary saving and reduced consumption, with a more moderate impact on inflation due to conflicting demand and uncertainty effects.