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

Banks must Constantly Adapt to Revised or New AML Legislation

Switzerland has had various AML / CTF regulations in place for quite some time. However, after 2020, banks were faced with a great number of adaptations due to the changing AML legislation.

• 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.

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.

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

The FATF Plenary adopted Switzerland’s Mutual Evaluation Report (MER) in October 2016. Based on the results of the MER, Switzerland was placed under enhanced follow-up. The third Enhanced Follow-up Report was adopted in January 2020. The fourth Enhanced Follow-up Report published in October 2023 analyses the progress assessed to remedy some of the technical compliance shortcomings identified in its MER. Re-ratings are awarded to reflect the progress made. 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 Public Edition of the Basel AML Index ranks countries with sufficient data to calculate a reliable risk score. It provides a snapshot of global ML/TF risks and progress by countries and regions over time.

Out of 128 countries, the GSA countries are as follows:

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.