1. SupTech is Gaining Traction
   • The report reveals that suptech is gaining traction, with 71% of financial authorities already engaged in suptech initiatives. However, most efforts remain in the experimentation stage, primarily focused on improving data collection and basic analysis.
   • The majority of suptech applications focus on consumer protection supervision (59%) and prudential supervision use cases (58%).
   • Suptech is enabling new supervisory use cases that would not otherwise be possible, such as ingesting massive online datasets like social media streams to conduct sentiment analysis, parse online reviews to assess risks or identify fraudulent fintech apps, and conduct real-time, on-chain analysis for digital assets supervision.
2. Significant Barriers
   • Budget limitations, data quality, and technical skills are the most significant barriers to implementing suptech.
   • There is a notable mismatch between the experiences of financial authorities and vendors when it comes to procurement, with tech providers urging public agencies to address legacy procurement processes.
3. Differences between Emerging Markets and Advanced Economies
   • Financial authorities in advanced economies (AEs) are early adopters of suptech, have sufficient digital infrastructure, assign dedicated suptech roles and departments, and have seen more substantial internal outcomes than those in emerging markets and developing economies (EMDEs).
   • EMDEs agencies tend to run suptech initiatives within the supervision department itself, are more interested in trainings, technical assistance, digital tools, and seek funding primarily for solutions design and development.

Case Study:  Central Bank of the Netherlands' - AML/CFT/PF Detection Tool

The report includes several case studies. One great example is how the Central Bank of the Netherlands (DNB) developed an outlier detection tool to enhance its AML/CFT/PF supervision. The tool uses machine learning algorithms to identify unusual patterns in the data submitted by financial institutions. The tool's primary purpose is to detect outliers that may indicate risks related to money laundering, terrorist financing, and proliferation financing.

See:  Financial Institutions and Regulators Alike are Showing Growing Interest in Fintech and Regtech Solutions.

• The tool was developed in-house by the DNB's Data Science team, which worked closely with the AML/CFT/PF supervision team. The tool uses a combination of unsupervised machine learning techniques, including clustering and anomaly detection algorithms, to identify outliers in the data.
• The tool has been successful in identifying outliers that were not detected by traditional methods.
• It has also helped the DNB to better understand the risks associated with different types of financial institutions and to prioritize its supervisory activities accordingly.
• The DNB plans to further develop the tool by incorporating additional data sources and improving the interpretability of the machine learning models. The bank is also exploring the possibility of sharing the tool with other financial authorities.

Our colleagues in integrity supervision can now do their work in a more efficient manner by selecting the riskiest files using data science.

This case study demonstrates the potential of suptech to enhance financial supervision and address complex challenges such as money laundering and terrorist financing. It also highlights the importance of collaboration between data scientists and supervisors in developing effective suptech solutions.