Money laundering has been a global issue for decades, which is one of the major threat for
economy and society. Government, regulatory and financial institutions are combating it …

With the upsurge of online banking, mobile payment, and virtual currency, new money-
laundering crimes easily conceal in the enormous transaction volume. The traditional rule …

Accurate data annotation is essential to successfully implementing machine learning (ML)
for regulatory compliance. Annotations allow organizations to train supervised ML …

Purpose With the growing interconnectedness of global markets brought about by
globalization and technological innovation, there is a heightened worldwide risk of money …

Monitoring financial transactions is a critical Anti-Money Laundering (AML) obligation for
financial institutions. In recent years, machine learning-based transaction monitoring …

Today's most immediate threat to address is terrorism. Terror organizations use illegal
methods to raise their fund, such as scamming banks, fraud, donation, ransom and oil. This …

This study explores the fusion of artificial intelligence (AI) and machine learning (ML)
methods within anti–money laundering (AML) frameworks using data from the US Treasury's …

Artificial intelligence (AI) has the potential to bring significant benefits to highly regulated
industries such as healthcare or banking. Adoption, however, remains low. AI's entry into …

Money laundering has significantly increased in recent years as a result of the quick growth
of financial systems and the emergence of cryptocurrencies. These crimes threaten the …

Money laundering has damaging economic, security, and social consequences, fueling
criminal activities like terrorism, human and drug trafficking. Recent technological …