Generalized Score Matching for Non-Negative Data

A common challenge in estimating parameters of probability density functions is the intractability of the normalizing constant. While in such cases maximum likelihood estimation may be implemented using numerical integration, the approach becomes computationally intensive. The score matching method of Hyv\"arinen [2005] avoids direct calculation of the normalizing constant and yields closed-form estimates for exponential families of continuous distributions over $\mathbb{R}^m$. Hyv\"arinen [2007] extended the approach to distributions supported on the non-negative orthant, $\mathbb{R}_+^m$. In this paper, we give a generalized form of score matching for non-negative data that improves estimation efficiency. As an example, we consider a general class of pairwise interaction models. Addressing an overlooked inexistence problem, we generalize the regularized score matching method of Lin et al. [2016] and improve its theoretical guarantees for non-negative Gaussian graphical models.Comment: 70 pages, 76 figure

Still Keeping Secrets? Bank Secrecy, Money Laundering, and Anti-Money Laundering in Switzerland and Singapore

It was the Swiss Banking Act 1934 that first created numbered bank accounts, and in Switzerland, the principle of bank secrecy continues to be regarded as one of the primary aspects of private banking. Switzerland has long been accused of being one of the main tools of organised crime and the underground economy both by governments and Non-Government Organisations (NGOs), particularly after the class action suit against the Clearstream scandal, the Vatican Bank, and the 9/11 terrorist attacks. In addition to Switzerland, Singapore was ranked 5th on the Financial Secrecy Index (FSI) in 2018,  and faced a delicate conundrum because of the signs of crisis in emerging economies such as Indonesia and India, and came under growing pressure from the U.S. and Europe, which accused it of providing unfair advantages in the competition of tax havens. This article discusses money laundering and bank secrecy in Singapore and Switzerland primarily, and discusses whether they are still keeping financial information as secret as before because of its link to Anti-Money Laundering (AML) and Bank Secrecy

The Belt and Road Initiative (BRI) and its associated potential criminal risks

The Belt and Road Initiative (The Silk Road Economic Belt and the 21st-century Maritime Silk Road) is of great importance in the exchange of information, ideas, and technology among countries, as it benefits a sustained economy and innovation, and enhances China’s open economy as well. However, there also are risks associated with this venture, including cross-border criminal activities, such as money laundering and terrorist financing. This article explores the challenges the BRI faces and the necessity to combat those risks. It suggests further that particular attention needs to be paid to these issues to allow us to understand the related challenges in advance to be able to implement effective methods to reduce and/or prevent these risks from emerging

Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis

This paper focuses on the application and optimization of LSTM model in financial risk prediction. The study starts with an overview of the architecture and algorithm foundation of LSTM, and then details the model training process and hyperparameter tuning strategy, and adjusts network parameters through experiments to improve performance. Comparative experiments show that the optimized LSTM model shows significant advantages in AUC index compared with random forest, BP neural network and XGBoost, which verifies its efficiency and practicability in the field of financial risk prediction, especially its ability to deal with complex time series data, which lays a solid foundation for the application of the model in the actual production environment