The Fight Against Money Laundering in Switzerland

In January 2023 the revised Swiss Anti-Money Laundering Act came into force, together with corresponding ordinances and the AMLO-FINMA. In response, the banks have implemented the regulatory requirements imposed – at great expense in some cases. But legislative and regulatory hurdles are not the only driver behind this change: cutting costs has also become a key goal. And it’s something more and more institutions are relying on artificial intelligence to achieve.

Revision of Swiss Anti-money Laundering Ordinances and Laws

Switzerland has seen several regulatory changes to ramp up efforts to stop money laundering initiated in recent years. They are intended to strengthen Switzerland’s defence mechanisms:

• 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 Jan 1, 2020.
• In October 2022, 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.
• In March 2021, the Swiss Parliament had adopted the amendment to the Anti-Money Laundering Act (AMLA). During its meeting on 31 August 2022, the Swiss Federal Council brought the revised AMLA and the amended Anti-Money Laundering Ordinance (AMLO) into force with effect from 1 January 2023.

The key changes can be summed up as follows:

• Verification of the beneficial owners
• Updating of client data and suspicious activity reports concerning money laundering (Know Your Customer)
• Transparency of associations at heightened risk of terrorist financing
• Stronger supervision and controls on precious metals

Reports of Suspected Money Laundering Are on the Up: A Problem for Banks and MROS

Over and above banks alone, the Money Laundering Reporting Office Switzerland (MROS) is also being called upon. Year on year, the mountain of suspected money laundering reports grows ever higher and MROS must take action. In May 2022 the Swiss Money Laundering Reporting Office MROS published its actual annual report. According to it, 6,000 suspicious activity reports were received in 2021, marking a 12% increase compared to 2020.

The introduction of goAML meant the method used to count suspected cases was adjusted. Estimates show that the 5,964 reports in 2021 correspond to 10,735 business relationships.

FATF Country Audit on Anti-money Laundering in Switzerland

Another factor stressing those involved in money laundering prevention is the country audit by the FATF. In January 2020, it published the Third Enhanced Follow-up Report, which was intended to examine progress since the last country audit in 2016. In its report, the FATF confirms compliance, marking eight of the 40 recommendations as “compliant” (C). Switzerland is classed as “largely compliant” (LC) for 27 recommendations and “partially compliance” (PC) for five recommendations.

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:

When It Comes to Anti-money Laundering, Banks Have Most Leverage

According to the MROS, 90 percent of money laundering reports in 2021 came from the financial sector. These statistics also show that other sector players, like payment service providers, credit card issuers or asset managers, are inconsequential by comparison, which has been more or less the case for years.

Five 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.

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.