18.12.2024

Why is the Fight against Money Laundering in Switzerland an Ongoing Challenge?

Switzerland has had various AML / CTF regulations in place for quite some time. The Swiss National Council plans additional regulations for 2025 regarding a new transparency register and the identification of beneficial owners. 

Over the past few years, financial service providers have adapted to the evolving regulatory requirements, often at significant effort and resource expense. However, regulatory compliance is not the only driver; improving efficiency has also become a key objective. 

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Banks must Constantly Adapt to Revised or New AML Legislation

Changes to AML regulations in Switzerland have been introduced over recent years through different laws and international agreements:

  • The Swiss Banking Association (SBA) published the revised Agreement on the Swiss banks’ code of conduct with regard to the exercise of due diligence (CDB 20). It came into force on January 1, 2020.
  • The Swiss Financial Market Supervisory Authority FINMA partially revised the FINMA Anti-Money Laundering Ordinance (AMLO-FINMA), which came into force on 1 January 2023.
  • The Swiss Federal Council brought the revised AMLA and the amended Anti-Money Laundering Ordinance (AMLO) into force with effect from 1 January 2023.
  • On February 27, 2024, Switzerland signed a bilateral agreement of cooperation with Panama to fight financial crime and in particular money laundering, financing of terrorism, and bribery and corruption. Switzerland also has mutual legal assistance agreement with Indonesia and Kosovo.
  • At its meeting on 22 May 2024, the Federal Council adopted the dispatch on the further development of the AML framework to be submitted to Parliament. The aim is to reinforce the integrity and competitiveness of Switzerland utilising a federal register of beneficial owners and due diligence for particularly risky activities in legal professions, as well as other provisions.
  • On December 18, 2024, the Swiss Council of States approved the Federal Act on the transparency of legal entities and the identification of beneficial owners. The proposal now goes to the National Council. The transparency register is intended to prevent companies in Switzerland from being used for money laundering or the concealment of assets.
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Reports of Suspected Money Laundering Are on the Up: A Problem for Banks and MROS

The volume of incoming SARs continued to increase in 2023: The Swiss Money Laundering Reporting Office MROS received 11,876 SARs, which corresponds to around 47 SARs per working day. Compared to 2022 (7,639), this amounts to a 55.5 % increase.

According to the MROS, 90.5 per cent of money laundering reports in 2023 came from the financial sector.

ACTICO AML Statistics 2023

FATF recognizes Switzerland’s progress in strengthening its AML/CTF measures

The fourth FATF Enhanced Follow-up Report published in October 2023 analyses the progress assessed to remedy some of the technical compliance shortcomings identified in its MER. Eight of the 40 recommendations are now marked as “compliant” (C). 29 as largely compliant (PC). Three recommendations are classified as “partially compliant” (PC). The next FATF mutual evaluation of Switzerland is expected to take place in 2027/2028.

Basel Anti-Money Laundering Index: Assessing Money Laundering Risks Worldwide

The Basel AML Index measures the risk of money laundering and related financial crimes in countries and jurisdictions around the world. It uses a composite methodology, with 17 indicators in five domains in line with key factors considered to contribute to a high risk.

Out of 164 countries, San Marino received the highest rating in 2024. At the very bottom, in position 164, is Myanmar. Switzerland ranks 124th, while Liechtenstein ranks 139th.

ACTICO Basel Index 2024

Five Ways Financial Service Providers Can Prevent Money Laundering More Effectively

Regardless of the place of business, the following points can be helpful for financial service providers to improve their anti-money laundering activities.

  1. 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
  2. Clearly state current persons and entities of interest following updated sanctions PEP and embargo lists
  3. Integrate machine learning methods, e.g. to help reconcile data with sanctions lists and monitor embargos during payment transactions
  4. Use machine learning insights to reduce the rate of false positives, verify previous clarifications and streamline clarification process to cut costs
  5. Conduct efficiency and effectiveness tests as part of payment monitoring

Retail Bank Uses Machine Learning to Save Around 40 Per cent of False Positives in Money Laundering Detection

In collaboration with ACTICO, a retail bank rubber-stamped the potential of AI as a means of reducing false positives in the battle against money laundering. The bank used a base of almost 12,000 historical money laundering anomalies to train a machine learning-model to predict which would require further investigation. The model learns from transaction and customer data for which the anomaly was flagged, as well as whether the anomaly required investigation in greater depth to clarify. A validation dataset demonstrated the potential to eliminate around 40 per cent of false positive cases, without overlooking any SAR which would otherwise be notifiable to the financial regulator.

Conclusion

Banks often find themselves overwhelmed with the repositioning required when money laundering laws are amended, particularly given the shortage of human resources to handle such work. Financial institutions are inherently cost-conscious, particularly in non-revenue-generating areas. Consequently, banks are increasingly scrutinizing their internal processes to enhance efficiency and reduce costs. Machine learning, an artificial intelligence component, offers enormous potential to reduce costs. One of the top priorities is to leverage machine learning to reduce the number of false positives, which has already achieved convincing results in practice.

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