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

In October 2023, the Financial Action Task Force FATF published Switzerland’s fourth follow-up report. The FATF acknowledges the progress. Nevertheless, further improvements are needed.

Since the publication of the third follow-up report in 2020, Switzerland has seen several regulatory changes to ramp up efforts to stop money laundering. Banks have implemented the regulatory requirements imposed – at great expense in some cases. However, regulatory hurdles are not the only driver: cutting costs has also become a key goal. This is why more and more institutions are relying on AI.

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 August 30, 2023 the Federal Council filed a draft law to strengthen the fight against money laundering for consultation. The aim is to reinforce the integrity and competitiveness of Switzerland with a federal register of beneficial owners, 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

In 2022, MROS received 7,639 SARs. This is an increase +28% over the previous year. According to the annual report 2022, MROS sees several reasons, the most important ones being the increased awareness of financial intermediaries to the issue of money laundering, legal adjustments particularly in connection with the definition of ‘reasonable suspicion’, and progress in digitalisation, e.g. better tools for transaction monitoring and internal analysis.

According to the MROS, 92 percent of money laundering reports in 2022 came from the financial sector. These statistics also show that other sector players are inconsequential by comparison.

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

A KPMG Study Predicts Growing Demand for Regtech Solutions

A publication released by the Swiss News Portal Finews dated 17.2.2023 refers to the latest Pulse of Fintech study issued by the KPMG consulting firm. According to this study, catalysts for investment in regtech solutions include the complex regulatory environment for financial services and the focus on profitability and cost-cutting. Companies are expected to rely on technology to streamline and improve their compliance activities. This includes AI-powered fintech solutions, especially in data analytics, real-time risk assessment and customer engagement.

Retail Bank Uses Machine Learning to Save Around 40 Percent Of False Positives in Money Laundering Detection

ACTICO’s proof-of-concept (POC) established with a retail bank rubber-stamped the immense 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 percent 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 are very cost-conscious by nature, particularly in areas that do not generate income, which is why banks are scrutinising their internal processes ever more closely. 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.