newsletter #9

What does “AML” mean exactly?

If you have ever indulged in mobster movies or tv shows, the concept of money laundering most likely is not foreign to you. Money laundering is the method by which “dirty money” received from criminal activities is processed through legitimate businesses and converted into “clean money”, in order for it to not be easily traced to the person originating the transaction or to the criminal origin of the funds. Hence, the criminal can now do what they like with their money.

WAIT! But doesn’t this mean that the pandemic might increase fraudulent behaviour?

The pandemic has forced everyone to reinvent the ways that they did business, for the entire world has had to do their activities in a socially distanced manner. With this, a lot of businesses had to sink or swim, having to choose between closing entirely or transition into digital platforms as a solution to the COVID-19 restrictions. Consequently, there has been an exponential rise in digital behaviour in everyone’s daily lives.

Are machine learning models cracking under the sudden behavioral changes caused by the coronavirus pandemic?

Machine learning is a kind of artificial intelligence (AI) that teaches computers to process information in a humanly way, by learning and improving upon past experiences. This allows corporations to transform processes that were previously only possible for humans to perform.

In what way do machine learning models fuel AML?

How can you prevent scams as a financial institution? 3 steps for safe transactional behaviour

For financial institutions to avoid facing fines and experience increased regulatory scrutiny, they should consider developing powerful AML programs. In 2020, over $10.4 billion in fines were imposed by global regulators on financial institutions for non-compliance of Anti-money laundering regulations. Therefore, as financial institutions it is important to take measures to ensure safe transactional behaviour. has an article on steps institutions can take to avoid fines, of which they mention:

Breaking Banks Europe

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Supervising crypto assets for Anti-money laundering (BIS)

This paper assesses AML/CFT supervisory practices relating to CPS and pays particular attention to emerging practices and common challenges faced by financial institutions considering the recent supervisory frameworks for crypto assets. While significant progress has been made by SSBs and financial authorities, it is still essential to implementing the FATF standards wherever that has not taken place yet.

We also want this newsletter to have a space for our partners and sponsors who supported the acceleration of FinTech ecosystems worldwide

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