RegTech

Key Global RegTech investment stats in Q1 2025: Global RegTech funding increased by 18% QoQ US asserted its dominance in the market by securing...

Money laundering is a serious global issue that enables grave crimes such as drug trafficking, human smuggling, and terrorism. For businesses handling significant financial transactions, it is crucial to establish robust anti-money laundering (AML) measures. These include detailed Know Your Customer (KYC) and Know Your Business (KYB) processes that involve rigorous identification, verification, screening, and monitoring of both individual and corporate clients.

The Hong Kong Government has announced a significant step forward in combating financial crime with the introduction of the Banking (Amendment) Bill 2025.

Deutsche Bank’s investment arm, DWS, has been fined €25m ($27m) for misrepresenting its sustainable investment strategies, as confirmed by the Frankfurt prosecutor’s office.

In 2025, the ability to effectively identify Politically Exposed Persons (PEPs) has become crucial in the financial sector's battle against money laundering and corruption. PEPs, due to their influential roles, are at higher risk of engaging in illicit activities.

In 2024, over $3.1 trillion of illicit funds circulated globally, with money mules playing a pivotal role in disguising these transactions. Identifying these mules is crucial for financial institutions aiming to mitigate risks, escape severe fines, and protect their reputations. Modern AML (anti-money laundering) software is essential for spotting potential mules, helping prevent significant monetary and reputational damage.

In an increasingly interconnected world, geopolitical shifts play a crucial role in shaping financial regulations. As global power dynamics evolve—whether due to trade wars, economic sanctions, conflicts, or shifting alliances—governments and regulatory bodies must adapt to safeguard financial stability.

Trustfull, a leader in identity intelligence for fraud prevention, today announced the launch of its new Login Solution.

In the intricate game of cat and mouse played between law enforcement and criminal organizations, the latter often seem to have the upper hand. These groups, including terrorist networks, are becoming increasingly sophisticated, using dual-use technologies to disguise their illicit activities.

While the integration of artificial intelligence (AI) in anti-money laundering (AML) processes promises enhanced efficiency, the assumption that AI lacks bias because it isn't human is misleading. AI systems, fundamentally designed for pattern detection, can inadvertently inherit biases from the data they train on.