Nos derniers articles

The article discusses the use of #deeplearning and #datamining in business intelligence protocols to optimize data-driven decision-making and improve efficiency. The authors focus on the use of Graph Neural Network and Autoencoders Models to process large amounts of data and model #fraud behaviors. They suggest that deep learning can be used to control #moneylaundering in financial institutions and improve visibility and transparency in businesses.

This paper discusses the efficiency, effectiveness, and costs of #denmark's #antimoneylaundering (#aml) #compliance standards. Although the country has caught up with international standards, the current global AML compliance system is ineffective in deterring #moneylaundering by professional actors. The system imposes significant costs on #banks and society, while spending too much time on minor infractions. To improve the system, the author argues for a #risk-based approach that automates large portions of the compliance process and allows compliance staff to focus on investigations.

"This paper aims to improve the ability of financial institutions to develop risk-based policies, procedures, and controls that are reasonably designed to (a) detect red flags relating to international wire transfers for the purchase of shares and (b) to investigate money laundering (ML) and terrorist financing (TF) through the trade system."