The publication of the so-called Swiss Secrets has turned all eyes on the financial hub of Switzerland and its efforts to fight money laundering. Despite several Swiss legislative initiatives in recent years, the data leak could not have been worse timed with the FATF country audit in 2022 in mind.
Hurdles Hampering the Swiss Fight Against Money Laundering in 2022
Revision of the Anti-Money Laundering Act in Switzerland
Recent years have seen Switzerland launch a number of initiatives to bolster its anti-money laundering defences. In 2020, the Code of Conduct on Due Diligence for Banks (CDB 20) came into force together with the revised Money Laundering Ordinance-FINMA (MLO-FINMA). Meanwhile, the revised Anti-Money Laundering Act Switzerland, a reform passed by the Swiss parliament in March 2021, is scheduled to come into force in mid-2022.
The goal of revising the Anti-Money Laundering Act was to implement some of the key FATF/GAFI recommendations and improve Switzerland’s ability to guard against money laundering and terrorist financing.
Here are the key elements of the changes:
- Verification of the details of the beneficial owner (UBO)
- Timeliness of customer data and money laundering reports (Know Your Customer)
- Transparency of associations
- Consolidation of supervision and checks in the precious metals sector
Even so, the changes were dismissed as window dressing or Mini Reform.
Money laundering SARs on the rise: banks and MROS face problems
And it’s not only banks feeling the heat – pressure on the Money Laundering Reporting Office of Switzerland (MROS) is also rising, as the number of money-laundering SARS cases soars ever-higher each year. Its most recent annual report notes record numbers of SARs received in 2020.
FATF country audit 2022 to prevent money laundering in Switzerland
The country audit by the FATF is another stressor. It issued its third follow-up report in January 2020, focusing on progress since the last country review in 2016. Meanwhile, the fifth such audit is expected in 2022.
According to the FATF, customer due diligence measures under the know-your-customer principle were not always satisfactory. The way data was updated for long-standing banking customers and asset managers and risk classifications came in for particular criticism, since wrongly categorising a business can lead to clarification errors.
When it comes to anti-money laundering, banks have the most leverage
According to MROS, 90% of money laundering reports in 2020 came from the financial sector. The statistics also show that other players in the sector, such as payment service providers, credit card issuers or asset managers, are inconsequential by comparison, which has been more or less the case for years. However, in 2020, when the COVID-19 pandemic peaked, reports from cantonal banks soared from 5.3% (2019) to 14%, which was attributed to money laundering in connection with COVID-19 loans.
5 ways financial service providers can prevent money laundering more effectively
Regardles of the place of business, the following points can be helpful for financial service providers to improve their anti-money laundering activities. In case of any questions about our products or your specific case, please do not hesitate to contact us.
- Improve their KYC profiles by including more detailed information on the customer or beneficial owner and verifying the risk class, origin of assets, expected inflows and outflows and forecast transactions per specific period
- Clearly state current persons and entities of interest following updated sanctions PEP and embargo lists
- Integrate machine learning methods, e.g. to help reconcile data with sanctions lists and monitor embargos during payment transactions
- Use machine learning insights to reduce the rate of false positives, verify previous clarifications and streamline clarification process to cut costs
- Conduct efficiency and effectiveness tests as part of payment monitoring
Brochure: Machine Learning in the Compliance Suite
Identifying money laundering risks more effectively and reducing false positives are some of the key applications for machine learning when applied to compliance services financial service providers. Learn how much you could gain from combining compliance officer insights with data science.
These might be of interest to you
Success Story: Anti-Money Laundering in Insurance Companies
Swiss Life is strengthening its security standard for combating money laundering. More information is available on our customer page.Read Success Story
White Paper: Better compliance through digitization with machine learning
Why successful banks are now turning to machine learning, including nine lessons learned. This white paper outlines the development of money laundering reporting and explains the key milestones to further automate the compliance process.Download Whitepaper
Sanctions and PEP screening with machine learning
Regulatory requirements necessitate matching new and existing customers against sanctions lists. PEPs must be recognized and treated with increased care. Learn how Machine Learning can support the Name and PEP Check.Learn more