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.
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.
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.
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.
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:
Regardless of the place of business, the following points can be helpful for financial service providers to improve their anti-money laundering activities.
Banks and insurance companies are reaching their limits when it comes to money laundering prevention – both in terms of personnel and cost-effectiveness. Artificial intelligence components such as machine learning simplify and accelerate processes. Our whitepaper provides details and shows a savings potential of 40 percent for false positives at a retail bank.
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.
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.
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.
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