Strise raises EUR 10 million in funding round

The funding round was led by Atomico with participation from existing investors. With the additional asset, Strise plans to expand internationally across key European markets, starting with the UK. The anti-money laundering (AML) intelligence software company aims to expand its customer base across the financial, insurance, and legal sectors.

Moreover, Strise will deploy the funding to further improve its end-to-end AML product offering and solidify its place in reducing financial crime and ensuring a more safer and transparent business sector. Company representatives have highlighted that as crime and regulations rise in complexity, traditional methods may not be enough to protect banks from financial crime. Automation can reduce costs and create a more effective process. Strise’s objective is to help AML and compliance teams work faster, detect crime more accurately, and maintain compliance. 

Currently, Strise’s solutions are used by 70% of the region’s banks, including Nordea, Handelsbanken, and Vipss MobilePay. Furthermore, the company’s reach extended past the Nordic area, with US-based law firm Orrick and UK-based EY being two of the first companies to join Strise’s Early Access Program in the UK.

The company combines a knowledge graph with AI and NLP technologies, as well as an intuitive user interface to offer compliance teams a solution that is suitable for their needs. As per officials, its existing customers have reported a 90% reduction in diligence time and a 30% cost saving since integrating the system.

According to Europol’s report, it is estimated by the United Nations Office on Drugs and Crime (UNODC) that between 2 and 5% of global GDP is laundered annually, resulting in amounts between EUR 715 billion and EUR 1.87 trillion. 

As money laundering and other financial crimes are increasing globally, financial institutions have started to implement different methods to reduce them. More banks turn to AI to prevent incredulous activities, as this technology may improve accuracy and efficiency in detecting financial crime.

AI algorithms can analyse increased amounts of data in real time, being able to identify patterns and anomalies and flag unusual transactions or activities for investigation. Moreover, with the integration of AI, financial institutions can reduce costs and use resources for other critical processes. For example, in 2019, HSBC implemented an AI-powered system to automate its AML processes. Through this, the bank was able to reduce the time required for AML reviews, improve the accuracy of their program, and save EUR 372,960.

While AI brings many advantages when implemented in AML programs, there are also several issues that financial institutions encounter, including data quality, explainability, regulatory compliance, and lack of human expertise.

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